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Novel Approach for Stock Prediction Using Technical Analysis and Sentiment Analysis 1

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4th Congress on Intelligent systems (CIS)

作者： Gaharwar, Gauravkumarsingh Pandya, Sharnil Navrachana Univ Vadodara Gujarat India Symbiosis Int Univ Pune Maharashtra India

ISBN: (数字)9789819990375

ISBN: (纸本)9789819990368;9789819990375

Stock prediction is not new;people have tried to predict stock price or good quality stocks for ages. Machine learning has opened a new direction for the problem of stock prediction. The critical factor deciding the success or failure of the machine learning model depends on the quality of features computed. Moreover, the concept of ensemble learning can significantly enhance the quality of prediction. The proposed model gathers data price time series data and news articles from open access domain and do necessary pre-processing. The model also calculates technical and sentiment features and uses them for training the ensemble model. Performance is compared to definite matrices and with other similar research.

关键词： Stock prediction Technical analysis Sentiment analysis Support vector machine Ensemble learning

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Impact of Artificial Intelligence Tools and Text-to-3D Model Generators on Interior Design 8th

Impact of Artificial Intelligence Tools and Text-to-3D Model...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Shreya, H. R. Kumar, Tarun PES Univ Bengaluru 560085 Karnataka India Indian Inst Sci Bengaluru 560012 Karnataka India

ISBN: (纸本)9789819713127;9789819713134

The advent of Artificial Intelligence (AI) tools in the present world is significantly impacting the field of interior design and architecture. This is due to the use of AI tools in 3D modelling and design, through computer-aided design (CAD). Therefore, the issue of inclusion of AI tools in design education arises, and a method to do so by using AI services which are accessible to students is needed. With this objective in mind, applications of CAD and AI were explored to gain a basic understanding of 3D modeling and prototyping in interior design. This led to the realisation that furniture is one of the main components of an interior design project. A few categories of furniture were then finalised based on their importance in interior design and a dataset was created to gather images of real furniture against 3D modelled furniture, both AI-generated and self-made. Google Teachable Machine is then used to create an image model to categorise furniture and detect deformities in the images of furniture in the dataset. The image model is then tested, following which a smartphone application implementing the above-mentioned concept is proposed, which is publicly accessible. The results of the testing elucidated the need for a service to help with the scanning of 3D models. This paper seeks to aid design students in accessing AI tools and better the design pedagogy by equipping them with the skills necessary to master AI tools. This paper also paves the way for further innovation in AI tools which are inclusive and accessible to students, by developing the proposed solution appropriately.

关键词： Artificial Intelligence (AI) Interior design 3D modelling Computer-aided design (CAD) Design pedagogy Google Teachable Machine

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Incidence Assessment of Diabetes by Delegation in the United Mexican States Applying the Multilayer Perceptron Neural Network 12th

Incidence Assessment of Diabetes by Delegation in the United...

引用

12th World Conference on Information systems and Technologies (WorldCIST)

作者： Cardenas-Isla, Hubet Reyes-Osorio, Rodrigo Leonardo Jacobo-Rojas, Adrian Robles-Gallegos, Ashlee Yail Marquez, Bogart Inst Tecnol Tijuana Calzada Tecnol S-N Tijuana 22414 Baja California Mexico

ISBN: (纸本)9783031602146;9783031602153

The prevalence and impact of diabetes in Mexico are thoroughly examined in this study. Over the past four decades, diabetes has emerged as the predominant health concern in the country, ranking as the leading cause of death in women and the second in men since 2000. It has also been identified as the primary culprit behind premature retirement, blindness, and kidney failure. Projections indicate that by 2025, nearly 11.7 million Mexicans could be diagnosed with diabetes, underscoring the urgency of understanding and addressing this escalating health crisis [1]. Previous research on diabetes characteristics and consequences among individuals aged 20 to 40 has primarily relied on hospital-based samples, potentially skewing results toward severe cases or specific ethnic groups. A critical gap exists in nationwide, population-based studies that can provide a more comprehensive understanding of the prevalence and characteristics of early-onset type 2 diabetes. Given that 79% of Mexico's population is under 40 years old [2], there is an imperative need for such studies to inform targeted preventive measures. This study aims to fill this gap by predicting the risk index for the general population based on diabetes incidence data collected by a delegation in Mexico through public health institutions. The prediction will leverage a multilayer perceptron neural network to enhance the accuracy and applicability of the findings.

关键词： Diabetes Obesity Data mining Neural network Epidemiology Ethnic group Deep Learning

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Using the Decision Tree Model Method for Data Analysis 9th

Using the Decision Tree Model Method for Data Analysis

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

作者： Tynchenko, Vadim S. Nelyub, Vladimir A. Gantimurov, Andrei P. Kravtsov, Kirill I. Mikhalev, Anton S. Bauman Moscow State Tech Univ Artificial Intelligence Technol Sci & Educ Ctr Moscow 105005 Russia Reshetnev Siberian State Univ Sci & Technol Inst Comp Sci & Telecommun Informat Control Syst Dept Krasnoyarsk 660037 Russia Far Eastern Fed Univ Vladivostok Russia Reshetnev Siberian State Univ Sci & Technol Krasnoyarsk 660037 Russia Siberian Fed Univ Krasnoyarsk 660041 Russia

ISBN: (纸本)9789819732982;9789819732999

The publication introduces an overview of modern data analysis methods and technologies used in forecasting tasks, and considers key aspects of using data analytics in the context of professional football. In a number of regions of the world professional football is widespread, in which participation in football games becomes the main profession of players. On this basis, professional football clubs are organized and playing in them becomes part of a player's professional career. Particular attention is paid to the application of data analysis techniques to predict various factors as well as to assess the quality of the predictions.

关键词： Decision tree Data analysis Machine learning Predictive modeling Classification

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Remote Learning of Mathematics for Visually Impaired Students During COVID-19: Exploring Online Intervention, Resources, Challenges and Issues 1

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Intelligent systems Conference (IntelliSys)

作者： Shoaib, Muhammad Fitzpatrick, Donal Pitt, Ian Univ Coll Cork Sch Comp Sci & Informat Technol Cork Ireland Natl Disabil Author Ctr Excellence Univ Design Dublin Ireland

ISBN: (数字)9783031477157

ISBN: (纸本)9783031477140;9783031477157

Due to COVID-19, several dramatic changes have appeared all over the world i.e., travel restrictions, healthcare shortages, self-isolation, economic crises, social distancing, increases in food demand, job losses and closure of educational institutions. This led to a rapid transition from face-to-face to remote teaching. Most remote learning platforms are designed primarily for sighted students and are less useful for visually-impaired students. Especially in mathematics, it's quite difficult for visually-impaired students to access rich visual information, such as graphs, algebraic notations, geometric shapes and statistical formulas. This article provides an overview of mathematics learning resources during the COVID-19 pandemic. It explores assistive technology-based interventions which enable visually-impaired and blind students to actively participate in remote teaching and enhance their learning skills. It was noted that solutions are available for various platforms (i.e., smartphones, tablets, laptops & desktop PCs) to support visually-impaired students. Assistive Technology in remote learning also plays a key role in enhancing the mathematical skills of visually-impaired students. Furthermore, we have investigated the important COVID-19-related resources of different institutes which are very beneficial for the online education of visually-impaired students. Moreover, some challenges and issues are discovered such as internet connectivity, lack of interaction, inadequate support, one-way communication and less effective learning. Finally, some future directions are suggested for research i.e., parents should be a part of remote learning.

关键词： Learning Mathematics COVID-19 Visually impaired e-Learning

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Packet Detection and Classification by Sequential Data Decoding 13th

Packet Detection and Classification by Sequential Data Decod...

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13th Computer Science Online Conference

作者： Ivan, Melnik Georgy, Kalygin Konstantin, Kulikov Vladimir State Univ Gorkogo Str 87 Vladimir 600000 Russia

ISBN: (纸本)9783031705175;9783031705182

The article considers an algorithm for detecting and classifying a packet in the absence of a priori information about the packet structure. The algorithm allows to optimize hardware costs when implementing a receiving device. The structure of the demodulator is given. The algorithm can be used to expand the coverage area of 5G/6G systems, implement multi-channel heterogeneous communication systems. The results supported by the State Assignment of the Vladimir State University (FZUN-2023-0004). #CSOC1120.

关键词： packet data transmission serial decoding hardware resources

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Reinforcement Learning for Process Mining: Business Process Optimization 12th

Reinforcement Learning for Process Mining: Business Process ...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Soliman, Ghada Mostafa, Kareem Younis, Omar Orange Innovat Egypt Giza Egypt

ISBN: (纸本)9783031602269;9783031602276

Process mining aims to extract knowledge from event data to understand, analyze, and improve Processes. Utilizing the benefits of Reinforcement Learning enables the automation of the business processes discovery, by systematically exploring the state space based on a certain environment interaction to achieve a certain goal, that reduces time and effort required for manual analysis and decision-making. This paper proposes an automated system capable of discovering certain organization's business processes and identifying the optimal sequence of transitions towards the ticket terminal state avoiding the bottleneck. To identify the structure of the business processes, the available source/target transitions are extracted, along with the computed transition probabilities between each possible transition. These probabilities are then incorporated into the reward design, which is needed to adjust the agent's behavior. By applying the Q-Learning algorithm, a Q-function learns the quality (q-value) of taking each possible action, given a certain state, encoded as a table that is updated iteratively during the training until reaching an optimal policy. Finally, after a certain number of episodes, the optimal sequence of transitions is identified. The Deep Q-Network (DQN) algorithm is applied to compare the results between the two approaches, and the Q-Learning agent tends to produce the optimal path that is aligned with the business processes.

关键词： Process Mining Business Process Optimization Machine Learning Reinforcement Learning Q-Learning Deep Q-Network

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Phrase-Based Topic Discovery from Spanish Social Media Texts 1

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Intelligent systems Conference (IntelliSys)

作者： Lopez, Alejandro Reyes-Ortiz, Jose A. Tovar Vidal, Mireya Bravo, Maricela Sanchez-Martinez, Leonardo D. Univ Autonoma Metropolitana Dept Sistemas Azcapotzalco 02128 Mexico Benemerita Univ Autonoma Puebla Fac Comp Sci Ave San Claudio & 14 Sur Cu 72570 Mexico

ISBN: (数字)9783031477188

ISBN: (纸本)9783031477171;9783031477188

Recently, there has been a large amount of valuable information on various topics in social networks. For example, many worldwide appreciate clinical information about suffering, signs, symptoms, procedures, drugs or treatments related to diseases. However, this information needs to be more structured since it is in natural language, which makes it difficult for readers to find respected data manually. In addition, it is a tedious, expensive and time-consuming process. Clinical information of interest, such as disease names, is usually expressed in phrases, not just words. It requires computational methods based on Natural Language Processing for a phrasal textual analysis to find topics of interest from texts. Therefore, this paper presents an approach for automatic topic discovery from social media texts in Spanish about three prevalent diseases in Mexico: COVID, Diabetes, and Cancer. This approach introduces an analysis based on phrase level as entries for LDA and BTM topic discovery algorithms. An evaluation process has been performed using the coherence topics metric and relevant phrases obtained for discovered topics. The results show encouraging 0.7463 coherence for COVID texts using noun phrases and the BTM algorithm.

关键词： Natural language processing Textual phrases Topic discovery Nominal phrases

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Towards a Risk-Focused Sales and Operations Planning Process 7th

Towards a Risk-Focused Sales and Operations Planning Process

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7th International Conference on Logistics Operations Management (GOL)

作者： Fakhry, Danielle Oger, Raphael Lauras, Matthieu Univ Toulouse Ind Engn Ctr IMT Mines Albi Albi France

ISBN: (纸本)9783031686276;9783031686283

Sales and Operations Planning (S&OP) is a vital business process that establishes a connection between the strategic plan of a company and its daily operational plans, facilitating the alignment of demand and supply for its products. However, in the face of dynamic and volatile business environments characterized by constant changes, achieving the desired objectives of S&OP becomes increasingly challenging. The explicit inclusion of a comprehensive understanding of uncertainties and decision options within the S&OP process has not been adequately addressed. In this context, the traditional S&OPprocess often falls short in addressing the unpredictable factors like demand fluctuations, supply disruptions, market changes, and other uncertainties. This paper aims to enhance the S&OP process, encompassing sub-processes such as demand planning, supply planning, and financial integration, with a dedicated focus on effective uncertainty management. The Risk-Focused S&OP Process proposed in this paper aims to equip organizations with a comprehensive set of strategies that will enable them to navigate the complicated supply chain road successfully. This approach seeks to address the critical need for strategies that can adapt to evolving uncertainties. The Risk-Focused S&OP Process enables organizations to address uncertainties and capitalize on opportunities, thereby promoting long-term sustainability and competitiveness. One of the limitations of this study is that the Risk-Focused S&OP Process, although introduced in this article, has not undergone validation. Consequently, future research should implement and validate this study to ensure its broader applicability.

关键词： Sales and Operations Planning (S&OP) Uncertainty Risk Management

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Development of a Decision Support System in a Canning Industry

Development of a Decision Support System in a Canning Indust...

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Intelligent systems Conference (IntelliSys)

作者： Mallioris, Panagiotis Kokkas, Georgios Styliadis-Heinz, Alexandros Margaritis, Ioannis Stergiopoulos, Fotios Bechtsis, Dimitrios Int Hellen Univ Dept Ind Engn & Management POB 141 Sindos 57400 Greece SAVVYCAN SA Karavaggeli 6 Kalohori 57009 Greece

ISBN: (纸本)9783031477140;9783031477157

Decision Support System frameworks have great importance in the context of Industry 4.0 to prevent production bottlenecks, machine malfunction and to increase the reliability of the industrial processes environment. With the development of digitalization, Decision Support systems (DSS) alongside Cyber Physical solutions, Internet of Things (IoT) devices and Big Data approaches constitute the main core of an industrially oriented smart manufacturing application. However, a considerable amount of industries lack the technological infrastructure in order to effectively utilize the vast amount of data, collected from various sensors and heterogenous sources scattered at various points of the production process on a daily basis. The scope of this paper is to present a conceptual framework of a DSS in a Canning Industry in order to utilize high-volume data collected from embedded sensors in the production process in order to detect and eliminate bottlenecks. The first part of the solution is dedicated to the integration of Programmable Logical Controllers (PLC) and the KEP Open Platform Communications (OPC) Server for the data acquisition and communication with a MySQL relational database, while the second part is on the data manipulation and the presentation of data analysis strategy for the decision making. Three machine learning models namely Random Forest, Naive Bayes and SVM were tested for the prediction of total production losses with Random Forest outperforming the rest with Accuracy of 90%. As a final step, a dashboard with real time descriptive statistics and an alarm-based system for upcoming potential failures are presented. This approach will improve the existing production process and act as a guideline for upcoming research in Decision Support systems in a Canning Industry with a significant replication potential in other types of industries.

关键词： Canning industry Big data Decision support system Data mining

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