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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Predicting Stock Closing Prices: A Deep learning Approach to Financial Forecasting 1

Predicting Stock Closing Prices: A Deep Learning Approach to...

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1st international conference for Women in computing, InCoWoCo 2024

作者： Elangovan, R. Santhi, B. Usha, J. Ranjith, B.K. School of Management SASTRA Deemed University Thanjavur613 401 India School of Arts Science and Humanities SASTRA Deemed University Thanjavur613 401 India

ISBN: (纸本)9798331518943

In an era of volatile financial markets, accurate stock price prediction is essential for informed investment decisions. This project tackles the challenges of forecasting stock closing prices for ***, ***, and *** using advanced deep learning models over a comprehensive 10-year dataset. Through innovative preprocessing methods - such as forward filling for missing values, normalization for model stability, and reshaping data into sequences for recurrent neural networks (RNNs) - the study employed three architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN). An ensemble approach that combined predictions from these models was explored to enhance overall prediction accuracy. However, the Gated Recurrent Unit (GRU) model consistently demonstrated superior performance, achieving the highest $\mathbf{R}^{\mathbf2024}$ score of 0.998185 and the lowest Mean Absolute Percentage Error (MAPE) of 4.68947 in dataset 1, along with strong results in dataset 3 with an $\mathbf{R}^{\mathbf2024}$ score of 0.994933. Although the LSTM model showed promise in dataset-2, it exhibited a significantly higher MAPE, indicating its limitations. The findings underscore the effectiveness of the GRU model for stock price prediction, highlighting the importance of selecting appropriate modeling techniques based on dataset characteristics while establishing a robust framework for financial forecasting using advanced deep learning methodologies. © 2024 IEEE.

关键词： Prediction models

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Big data-Driven Long Term Accurate CO2 Emission Forecasting Using Advanced Recurrent Neural Networks

Big Data-Driven Long Term Accurate CO2 Emission Forecasting ...

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2024 IEEE international conference on Big data, Bigdata 2024

作者： Verma, Anil Routhu, Lalit Chandra Chigrupaatii, Rishikant Choubey, Anurag Misra, Rajiv Singh, T.N. Indian Institute of Technology Patna Dayalpur Department of Computer Science and Engineering Daulatpur801106 India Bennett University Scset Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350362480

Global climate change driven by carbon dioxide (CO2) emissions necessitates accurate forecasting for effective mitigation strategies. Traditional forecasting models, such as ARIMA and statistical methods, often struggle to handle the non-linear and long-term temporal dependencies in CO2 emission data. This paper evaluates the performance of advanced Recurrent Neural Network (RNN) architectures, specifically Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) with attention mechanisms, in forecasting long-term CO2 emissions. These models are compared against traditional machine learning algorithms like Random Forest and Gradient Boosted Trees (GBT). The Random Forest and Gradient Boosted Trees, implemented using PySpark framework for distributed computing, are leveraged for efficient big data handling, enabling scalable solutions for CO2 emission forecasting. Our experimental results, using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), show that the Stacked LSTM model achieved the lowest MAE of 25.795, RMSE of 80.678, and MAPE of 1.072, outperforming Random Forest (MAE: 72.4, RMSE: 310.5, MAPE: 1.32) and GBT (MAE: 75.6, RMSE: 356.8, MAPE: 1.57). The findings demonstrate that advanced RNN models, particularly the Stacked LSTM and Bi-LSTM with attention, provide superior performance over traditional models, effectively capturing complex temporal patterns in the data. This study offers valuable insights into CO2 emission forecasting approaches, contributing to the development of accurate tools for climate policy and strategy formulation. © 2024 IEEE.

关键词： Low emission

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High-order functional connectivity based Major depressive disorder classification using self-attention based CNN 7

High-order functional connectivity based Major depressive di...

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7th international conference on data science and Information Technology, DSIT 2024

作者： Chenxi, Tian Meili, Lu Tianjin University of Technology and Education School of Information Technology Engineering Tianjin China

ISBN: (纸本)9798350384093

Major depressive disorder (MDD) is a common and socially significant psychiatric disorder with extremely complex pathologic mechanisms. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used for the classification and diagnosis of MDD. Most current studies reveal the relationship between MDD and brain function by extracting static low-order functional connectivity (SFC) between brain regions, often neglecting potential high-order interactions that may affect classification performance. Therefore, to fully exploit the more complex biomarker features between brain regions and enhance classification performance, this project extracts two types of dynamic high-order functional connectivity (dHOFC): dynamic topographic high-order functional connectivity (dtHOFC) and dynamic associative high-order functional connectivity (daHOFC). It fuses the features of these two types of dynamic high-order functional connectivity and proposes a self-attention-based CNN method for MDD classification validation. The results demonstrate that the proposed deep learning algorithm achieves high classification performance, with the fusion of dtHOFC and daHOFC features providing higher classification accuracy compared to using individual high-order functional connectivity features. © 2024 IEEE.

关键词： Deep learning

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Visual Detection of Marine Debris Using RTMDet 7

Visual Detection of Marine Debris Using RTMDet

引用

7th international conference on data science and Its Applications, ICoDSA 2024

作者： Bagus Padmanawijaya, Aryya Kosala, Gamma Sthevanie, Febryanti School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350365351

Marine debris, a hazardous threat to the marine environment, has emerged as a critical global environmental issue. The commonly applied method to remove marine debris from the marine environment is manual removal. One solution to this problem is to use deep learning-based visual marine debris detectors to automatically detect marine debris. However, in terms of marine debris detection, there are several challenges, one of which is because the complex marine environment makes visual detectors able to detect marine debris accurately and in real-time so as not to damage the marine environment. In this research, three variants of the RTMDet (Real-Time Models for Object Detection) model were trained and evaluated using the TrashCan-Instance dataset. One of them is the RTMDet-1 model with improvisation to replace the loss bbox in the head model with DIoU (Distance-Intersection over Union) and improvisation to add a sampling strategy with a Class-aware Sampling technique to handle the imbalanced data problem that has obtained mAP50(Mean Average Precision at threshold 50%) accuracy of 71.3%. This has made the model's best object detection accuracy on the TrashCan-Instance dataset while maintaining detection speed. These results prove that the model proposed in this study can be a vital consideration for further development in detecting marine debris. This contribution aims to address the global challenges related to marine debris and stimulate the development of more effective and efficient object detection models in complex marine environments. © 2024 IEEE.

关键词： Deep learning

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A Design for gamified platform for Children with ASD

A Design for gamified platform for Children with ASD

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Tuli, Harshita Kumar, Bibek Department of CSE Vishveshwarya Group of Institutions India Department of CSE LIET Greater Noida India School of Data Science Symbiosis University of Applied Sciences India

ISBN: (纸本)9798350355338

Autism spectrum Disorder (ASD) is a problem that affects a child’s nervous system, growth and development. It is called a spectrum because kids under ASD can restrict a child from developing social skills, facial expression or emotion in other people . A child with ASD may hesitate to be touched, may enjoy being isolated and are often rigid to routines being changed. The medical guidelines say that all children should be screened for ASD and other development disorders before age 2. These children may have delayed milestones. However, such delays can be overcome by the use of engineering approaches. In today’s digital era, kids with ASD are also exposed to gamified platforms. And as per the studies available the exposure to a gamified platform can provide a solution to associated delays for ASD children. With such a goal in mind, we have tried to work on engineering-based solution to train such kids so that they can overcome the learning delays. © 2024 IEEE.

关键词： Gamification

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Improving Transparency in Romanian Public Procurement: machine learning to Classify Bidders and Validate Decisions 28th

Improving Transparency in Romanian Public Procurement: Machi...

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28th international conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Silaschi, Iasmina Oana Pop, Ioan Daniel Coroiu, Adriana Mihaela Automation and Computing Faculty Politehnica University Timisoara Timis300223 Romania Computer Science Department Babes-Bolyai University Cluj-Napoca40084 Romania

The primary objective of this research paper is to conduct a real-life case study, centred around a substantial dataset procured from numerous Romanian public procurement tenders. The study aims to classify the bidders according to their compatibility and suitability, an assessment determined by the accuracy of different machine learning models. The dataset used, is a vast body of information. More precisely, it includes data from a total of 289,472 enterprises, 47,974 contracting authorities and 42,474 tenders. The use of this extensive data set ensures a robust and comprehensive analysis, thus enabling the extraction of meaningful insights and facilitating the drawing of reliable conclusions. In the quest to rank bidders effectively, the performance of nine intelligent algorithms is evaluated and compared. The top-performing algorithms in this context appear to be Decision Trees, along with ensemble methods derived from them, namely the Random Forest Classifier and the Extra Trees Classifier. The outcomes of this study are satisfying, outperforming the metrics of a similar study conducted in the same domain. Given the potential concerns about Romania's integrity in dealing with contracting companies for public procurement auctions, this study holds significant value in its ability to validate the decision-making process employed in previous auctions. By casting light on the effectiveness of past procurement decisions and offering strategic guidance for future acquisitions, this research holds substantial implications for improving transparency and streamlining the bidder selection process within the public procurement area. Therefore, with potential for future work upon it, it promises to become a valuable addition in enhancing the integrity and efficiency of public procurement in Romania. © 2024 The Authors.

关键词： extra trees classifier labelled data precision public procurement random forest classifier

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Technologies Integration With Semantic Web Techniques For Business Valuation: Identifying Equities' Intrinsic Value Without Market Bias 2

Technologies Integration With Semantic Web Techniques For Bu...

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2nd international conference on Cyber Resilience, ICCR 2024

作者： Faccia, Alessio Itani, Rania Eltweri, Ahmed Ae Wanganoo, Leena Pio, Leonardo Luigi University Of Birmingham Dubai United Arab Emirates Murdoch University Dubai United Arab Emirates Applied Science Research Center Applied Science Private University Amman Jordan Liverpool John Moores University United Kingdom Universita Di Foggia Italy

ISBN: (纸本)9798350394962

This text sets out to present an innovative approach to intrinsic equity value estimation, deploying state-of-the-art mechanisms and new technologies, but in particular, focusing on the integration of Semantic Web techniques. Its focus is on the problem of market sentiment, which commonly causes the value of equities to be far from their real worth. The proposed solution is to use in a mixed way sophisticated computational techniques and the capability of the Semantic Web's structured data handling to keep the valuations free of bias. The methodology employs advanced analytics, machine learning models, and Natural Language Processing (NLP) for the discovery of patterns and insights that may not easily be visible from traditional valuation methodologies. The holistic approach also involves qualitative analysis to enhance the understanding of the company's prospects. This approach integrates these technologies into the framework of the Semantic Web to provide a transparent and replicable way of determining the intrinsic value of equities, with minimum distortion by market sentiment. The paper argues that this method is quite opposite to traditional business valuation models that use a framework that emphasizes upon empirical evidence and methodological rigor. It postulates that, in the future, if these Semantic Web tools can be put into use for the systematic gathering of varied datasets for contextual analysis, the way valuation functions are handled will undergo a revolution. It would mean a shift in business valuation towards one that is more empirical and technology-enabled towards potentially changing investment analysis by methodological stringency and empirical data. CCS CONCEPTS. computing methodologies Modeling and simulation Model development and analysis Modeling methodologies © 2024 IEEE.

关键词： data handling

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Interdisciplinary Synergy: Resources for Embedding Plugged and Unplugged Computer and data science Activities into the K-12 Curriculum 14

Interdisciplinary Synergy: Resources for Embedding Plugged a...

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14th IEEE Integrated STEM Education conference, ISEC 2024

作者： Samaras, Ted Marlowe, Thomas J. Herbert, Katherine G. Anu, Vaibhav K. Hagiwara, Sumi Robila, Stefan A. Instructional Technology Coach Franklin Township Public Schools United States Seton Hall University Department of Mathematics and Computer Science United States School of Computing Montclair State University United States College for Education and Engaged Learning Montclair State University United States

ISBN: (纸本)9798350352801

In the ever changing environment of education, the skills learned and applied in computer and data science (CADS) have expanded beyond the reach of siloed, single-subject teaching in the classroom. The need for interdisciplinary learning and constructs have increased as real-world experiences have become a necessity and time has become a scarcity. The progression toward this reality has been accelerated in a post-pandemic world. This paper reviews literature regarding this topic, looks at barriers impacting student learning, and provides resources to assist educators in weaving plugged, unplugged, and hybrid components into their interdisciplinary lessons to amplify the impact of CADS. It also provides a scenario for and a vision of learning if educators can utilize an interdisciplinary approach to their lesson and unit plans. The sample template provided focuses on a middle and high school education model where multiple teachers are responsible for teaching the same students in a team. The curriculum discussed in the template is based on topics covered in the New Jersey State Standards for eighth grade students, but is flexible enough to be molded for the needs of various grade levels and different state and local standards. By using multimodal techniques and generating greater synergy through interdisciplinary lesson constructs, more robust learning environments are created for both students and educators. © 2024 IEEE.

关键词： data science

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Synthetic data Generation Using GANs for Improved machine learning Models 12th

Synthetic Data Generation Using GANs for Improved Machine Le...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Vinoth Kumar, S. Kumar, Kavuri Santosh Kotteeswaran, C. Manoj, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819789450

Explores the utilization of Generative Adversarial Networks (GANs) for generating synthetic images to address challenges related to limited data availability and privacy concerns. GANs, trained on diverse datasets, are capable of producing high-fidelity images from random noise. For example, training a GAN on the MNIST dataset allows for the creation of digit images that closely resemble those in MNIST, which can then be used to train other neural networks. Our evaluations demonstrate that these synthetic images maintain high fidelity and are effective in various tasks, including classification and object detection. This highlights the potential of GANs for dataset augmentation and enhancement of model performance, providing a viable solution for expanding training datasets while preserving data privacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Using machine learning to Unveil Early Signs of Parkinson’s Disease: A Review 5th

Using Machine Learning to Unveil Early Signs of Parkinson’s...

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Manoj, Rudraksh Sharma, Ankush Sharma, Sanjay School of Computer Science and Engineering Shri Mata Vaishno Devi University Katra India Neurology Shri Mata Vaishno Devi Narayana Super Speciality Hospital Katra India

ISBN: (纸本)9789819725496

Parkinson’s is a brain degenerative disease striking mostly over 60 s, which gradually steals movement. Tremors, stiffness, slowness, and walking troubles mark its motor symptoms. Later, fatigue, sleep issues, and thinking changes creep in. Degeneration in a brain region called Substantia Nigra, a midbrain region responsible for dopamine production, is the culprit. The cell death is caused by abnormal accumulation of proteins into Lewy bodies. This lack of dopamine disrupts both movement and other brain functions. Unfortunately, no cure has been found for this ailment till date, but early detection of the disease can help doctors to act at the right time in stimulating the deeper parts of the brain with L-DOPA and MAO inhibitors and the patient can return to a near normal lifestyle. Due to the deficit of dopamine in the brain, the communication between synapses is hindered and functions like speech, tremor, bradykinesia, rigidity, and stooped gait are the common symptoms. This is where machine learning comes in. With the help of the machine learning models like Support Vector machine (SVM) and Random Forest (RF) and voice (tremor) dataset of patients with Parkinson’s, high accuracy results in early detection can be reached. Precise application of steps like data preprocessing and feature extraction helped in improving the performance metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brain

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