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Index Tracking Via Learning to Predict Market Sensitivities 1

引用

Intelligent systems Conference (IntelliSys)

作者： Hong, Yoonsik Kim, Yanghoon Kim, Jeonghun Choi, Yongmin Mirae Asset Global Investments Co Ltd Seoul 03159 South Korea

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

Index funds are substantially preferred by investors nowadays, and market sensitivities are instrumental in managing index funds. An index fund is a mutual fund aiming to track the returns of a pre-defined market index (e.g., the S&P 500). A basic strategy to manage an index fund is replicating the index's constituents and weights identically, which is, however, cost-ineffective and impractical. To address this issue, it is required to replicate the index partially with accurately predicted market sensitivities. Accordingly, we propose a novel partial-replication method via learning to predict market sensitivities. We first examine deep-learning models to predict market sensitivities in a supervised manner with our data-processing methods. Then, we propose a partial-index-tracking optimization model controlling the net predicted market sensitivities of the portfolios and index to be the same. These processes' efficacy is corroborated by our experiments on the Korea Composite Stock Price Index 200. Our experiments show a significant reduction of the prediction errors compared with historical estimations and competitive tracking errors of replicating the index utilizing fewer than half of the entire constituents. Therefore, we show that applying deep learning to predict market sensitivities is promising and that our portfolio construction methods are practically effective. Additionally, to our knowledge, this is the first study addressing market sensitivities focused on deep learning.

关键词： Market sensitivity Index tracking Deep learning Portfolio optimization

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Index Matrix Representation of Data Storage Structures Using Intuitionistic Fuzzy Logic 1

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Bureva, Veselina Atanassov, Krassimir Genov, Miroslav Sotirov, Sotir Prof Dr Assen Zlatarov Univ Lab Intelligent Syst Prof Yakimov Blvd Burgas 8010 Bulgaria Bulgarian Acad Sci Inst Biophys & Biomed Engn Dept Bioinformat & Math Modelling Acad Georgi Bonchev StrBlock 105 Sofia 1113 Bulgaria

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

In the current research work a big data structure representation using extended intuitionistic fuzzy index matrix (EIFIM) is presented. The investigation is based on the theories of index matrices, intuitionistic fuzzy sets and databases. It is known that big data systems use different data structures. The data warehouses are implemented to integrate structured datasets. A data lake provides storing capabilities for unstructured data. Nowadays, the two concepts are integrated in data lakehouse platforms that provide facilities for structured, semi-structured and unstructured data. The open-source framework for managing and processing huge amounts of data Hadoop is observed. More precisely, the HDFS (Hadoop Distributed File System) organization is discussed. The aim of the investigation is to present big data structure using EIFIM. User access through big data system environment to reach files and other resources is analyzed. The intuitionistic fuzzy logic is applied to evaluate the big data system processes.

关键词： Big Data Databases Intuitionistic Fuzzy Sets Index Matrix

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New Cloth Unto an Old Garment: SOM for Regeneration Learning 15th

New Cloth Unto an Old Garment: SOM for Regeneration Learning

引用

15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM)

作者： Frota, Rewbenio A. Barreto, Guilherme A. Vellasco, Marley M. B. R. de Jesus, Candida Menezes Petr Brasileiro SA PETROBRAS Rio De Janeiro RJ Brazil Fed Univ Ceara UFC Ctr Technol Grad Program Teleinformat Engn Fortaleza Ceara Brazil Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn Rio De Janeiro RJ Brazil

ISBN: (纸本)9783031671586;9783031671593

A recent paradigm called Regeneration Learning addresses generative problems where the target data (e.g., images) is more complex than the available input source. While current cross-modal representation and regeneration learning rely on supervised deep learning models, this paper aims to revisit the adequacy of unsupervised models in this field. In this regard, we propose a new unsupervised approach that utilizes the SOM as a heteroassociative memory model to learn cross-modal representations in a topologically coherent map. This approach enables bidirectional predictive/regenerative mapping between domains. We evaluate the potential of this method for an unsolved (so far!) practical problem in petroleum geoscience.

关键词： Self-Organizing Maps Regeneration learning Variational Auto-encoders Petrophysical Logs Acoustic Image Logs

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A High-Accuracy Deep Learning Approach for Wheat Disease Detection 8th

A High-Accuracy Deep Learning Approach for Wheat Disease Det...

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Patil, Soham Lalit Modern Educ Soc Dept Comp Engn Coll Engn Pune India

ISBN: (纸本)9789819713196;9789819713202

Objectives: The purpose of the present investigation is to develop a reliable and practically deployable solution for farmers to detect wheat diseases. The proposed research aims to detect four major diseases namely, leaf rust, wheat loose smut, crown, and root rot, in the wheat crops, which is of paramount importance for controlling the spread and ensuring quality wheat production. Methods: This research uses pre-trained deep-learning (DL) models for detecting and classifying wheat diseases. The proposed investigation explores the usefulness of EfficientNetB0, VGG16, and ResNet50 for the detection of the said wheat diseases. The developed methodology is tested using a large dataset containing 4906 images of wheat plants with varying diseases. Findings: The EfficientNetB0 model achieved a 99.37% accuracy rate, outperforming the other models. The data augmentation and Multistage fine-tuning techniques, explored in this research enhance the performance of the model. Novelty: The literature reports a relatively low accuracy, for the detection of wheat diseases, which needs to be further increased from the perspective of reliability and practical deploytion. Moreover, the reported methods are verified on a limited dataset and diseases. It is also necessary to consider varying levels of infections of all commonly occurring wheat diseases. The proposed research methodology is experimented on a huge varying dataset of four diseases and obtains a high accuracy, thus addressing the gaps in the existing literature. The proposed DL-based approach exploiting the data augmentation and fine-tuning techniques for enhanced and reliable performance marks a leap towards practical deployment, making it novel.

关键词： Wheat diseases Deep learning Machine learning

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A Comparison Between the Most Used Process Mining Tools in the Market and in Academia: Identifying the Main Features Based on a Qualitative Analysis 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： de Vasconcelos, Gyslla Santos Bernardini, Flavia Viterbo, Jose Univ Fed Fluminense Comp Inst Rua Passo Patria 156 Niteroi RJ Brazil

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

There are several tools focused on process mining with different functionalities and purposes of use, ranging from the most intuitive to the extremely complex, configuring a difficult task to choose the tool that best applies to your work. With this in mind, a comparative study of the process mining tools used in the commercial and academic environments was developed. The work was developed from a bibliographic research of the business process management area, process mining, event logs definition, the XES standard, of the process mining tools, studies advanced about the process mining tools and identification of the most cited tools. Differences were verified between tools with academic proposals of the commercial ones, based on functionality, availability, customization possibility and support. In addition, the tools that simultaneously attend to these different kinds of audiences (commercial and academic) has been identified, were Celonis and ProM 6, respectively.

关键词： Process mining Event logs XES standard Commercial tools

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A Review of Plant Classification Using Deep Learning Models 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Karnan, A. Ragupathy, R. Annamalai Univ Fac Engn & Technol Dept Comp Sci & Engn Annamalainagar 608002 Tamil Nadu India

ISBN: (数字)9789819713202

ISBN: (纸本)9789819713196;9789819713202

Plant classification using deep learning models has witnessed significant progress in recent years, contributing to advancements in agriculture, environmental monitoring, and biodiversity conservation. This paper comprehensively analyzes the state-of-the-art deep learning models used for plant classification. This paper discusses various architectures, along with hybrid models and ensemble approaches. It examines the challenges and limitations of existing plant classification datasets and addresses the issues of data scarcity, overfitting, and interpretability. Furthermore, this paper explores the practical applications of deep learning in plant classification, such as agriculture, disease detection, and environmental monitoring. Finally, it outlines future directions, including multimodal data integration, explainable models, real-time classification, and ethical considerations. This comprehensive review will equip researchers and practitioners with valuable insights into the current landscape of plant classification using deep learning models, fostering further advancements in the field.

关键词： Plant classification Convolutional neural networks Recurrent neural networks Graph convolutional networks Capsule networks Siamese networks

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Digital Images Augmentation Using Mathematical Morphology Operators 9th

Digital Images Augmentation Using Mathematical Morphology Op...

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

作者： Abrudan, Dumitru Marcu, Ioana Manuela Vizireanu, Nicolae Natl Univ Sci & Technol Politehn Bucharest Bucharest Romania

ISBN: (纸本)9789819735556;9789819735563

Computer vision can be defined as a converter used by artificial systems to understand the information captured in digital images and videos sequences. Video sequences are digital images disposed at certain frames per second. To achieve excellent performance in the process of computer vision, it is necessary to compile a large amount of data which can be time-consuming and/or expensive. To alleviate such issues which consist in insufficient digital images to be processed during deep learning, different augmentation algorithms were developed, being classified in three types of categories: model-based, optimizing policy-based and model-free. Different approaches were carried out in the matter of images augmentation, but most did not consider a solution that would also reduce the dataset size. In this current approach, we evaluate the impact of mathematical morphology operators (MMO) on original digital images to achieve synthesized images using a model-based augmentation algorithm. Deep learning (DL) algorithms consisting in two trained deep convolutional neural networks (CNN) were involved in image processing. Using one CNN trained on original images dataset and the second CNN trained on augmentation images dataset with MMO, relevant results will be provided and will allow valid comparison.

关键词： Computer vision Deep learning Mathematical morphology Image processing Data augmentation

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Development of RAG System for Digital Transformation of Local Government and Considering Optimal Document-Segmentation Methods 9th

Development of RAG System for Digital Transformation of Loca...

引用

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

作者： Kawashima, Soki Shiramatsu, Shun Mizumoto, Takeshi Nagoya Inst Technol Gokiso ChoShowa Ku Nagoya Aichi 4668555 Japan Hylable Inc 2-203Suzuoto Bldg2-26-12 Toshima City Tokyo 1700005 Japan

ISBN: (纸本)9789819733040;9789819733057

Administrative documents are accumulating yearly, increasing the burden of document retrieval in administrative work. We developed a retrieval augmented generation (RAG) system for administrative digital transformation and tested its effectiveness. We presented participants with tasks related to administrative issues and compared the accuracy and time taken to respond using the RAG system with those using traditional manual systems. We also set up groups with varying levels of experience and knowledge, i.e., administrative officers in charge, officers from other departments, and students, to analyze the effectiveness of the RAG system in each group. For short-answer questions, the administrative officers in charge achieved a 93.3% correct-answer rate, comparable to manual work, while reducing working time by 23.6%. Students improved their correct-answer rate by 40.0% and reduced working time by 79.8% through the RAG system use. For descriptive questions, however, the system use led to lower evaluations. The evaluation for descriptive questions was based on accuracy of the response, completeness of the response, accuracy of the background, context, and reasons, and completeness of the background, context, and reasons. Retrieval accuracy increased with longer segments, with longer segments yielding higher correct-answer rate in short-answer questions and shorter segments resulted in higher evaluations for descriptive questions. We also explored the optimal document-segmentation character count for the RAG system in administration tasks.

关键词： Large language model Retrieval augmented generation Document segmentation Explainable artificial intelligence GovTech

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Limits and Development of an Educational Platform for Automation and ENSAP Integrating Moodle Regarding Client Experience 13th

Limits and Development of an Educational Platform for Automa...

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13th Computer Science Online Conference

作者： Arias, Ricardo Laos, Angel Pacheco, Michell Garces, Eduardo Univ Tecnol Peru Natalio Sanchez St 125 Lima Peru

ISBN: (纸本)9783031702846;9783031702853

Currently, the National School of Public Health (ENSAP) lacks a versatile and automated educational platform in Moodle to execute fundamental processes for the registration and filtering of participants, which is integrated with INFORHUS. Currently, ENSAP has an educational platform that completely lacks of the automation of such priority processes, which are carried out manually by the IT team in collaboration with external tools, which implies that they are quite slow and error-prone processes. Currently, participants register for the courses offered by ENSAP on its web portal. This configuration process for registration is carried out in an inefficient manner, which involves directly altering the database, which takes about 10min just to configure the course according to its requirements, after that, when registration is completed, the process of loading participants to the current educational platform is carried out manually, which could take between 20 to 45 min, which will also depend on the number of enrolled in the course. Based on the detailed problem, we proceeded with a systematic review of relevant research articles that are closely related, which provide important data for the resolution of the problem, in this way, a total of 139 research articles were found, of which approximately 38 articles were highlighted as very relevant to the problem. Therefore, in order to improve the final student experience and the efficiency of the processes in the technology area, the proposal of the papers is the evaluation of the limits, metrics and benefits of a new Moodle platform integrated with INFORHUS that automates the registration and mass upload process, which Specifically, it will considerably reduce the unnecessary time and effort of the ENSAP IT office.

关键词： Automation Registration process INFORHUS Educational platform Moodle Public health

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Decoding Consumer Insights: A Python-Powered Analysis of Social Media for FMCG Production Strategy

Decoding Consumer Insights: A Python-Powered Analysis of Soc...

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Intelligent systems Conference

作者： Mohammadian, Noushin Valilai, Omid Fatahi Constructor Univ Bremen Sch Business Social & Decis Sci Campus Ring 1 D-28759 Bremen Germany

ISBN: (纸本)9783031663352;9783031663369

In the fast-paced realm of Fast-Moving Consumer Goods (FMCG), where competitors abound and product life cycles are fleeting, the agility to meet consumer demands is key to market triumph. This paper advocates for the strategic integration of social media content analysis as a potent means to understand and act upon customer needs. Harnessing machine learning tools, particularly Python-powered sentiment analysis, our study explores social media platforms as rich repositories of real-time consumer sentiments, preferences, and trends. By deciphering these insights, companies can adeptly respond to market dynamics and proactively address consumer needs in upcoming product launches. Focusing on the efficiency of machine learning in distilling patterns from extensive datasets, this paper demonstrates the potential of these tools to augment competitiveness and responsiveness in the ever-evolving FMCGsector. Our findings underscore the instrumental role of social media analysis in shaping FMCG production strategy, offering a nuanced understanding of consumer behavior for informed decision-making.

关键词： Social media analytics PLM Sentiment analysis Consumer needs Omnichannel strategy

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