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TFT -DQN -A Dual Framework Model for Stock Market Forecasting 3

TFT -DQN -A Dual Framework Model for Stock Market Forecastin...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kalaivani, A. Asha, V. Sathya, J. Kabbin, Rahul B. Rahul Tej, Ravi Department of MCA New Horizon College of Engineering Karnataka India

ISBN: (纸本)9798331527549

The financial and economic outlook of any economy is effectively conveyed through the stock markets which in turn plays a very critical part in the global economy. Investors buy and sale stock to grow financially and earn investment returns and on the other hand, companies sell stock to gather funds. But then again, the share market is very volatile as the risk is high and there is uncertainty in it. Trends in the stock market can be accurately described as difficult tasks as the methods used in today’s market are not successful enough in understanding the different aspects of how consistently changing markets work. This research seeks to build on the existing literature on stock market prediction and decision making in terms of trading by presenting a new model, Dynamic Temporal Fusion Transformers (D-TFT). The model integrates Temporal Fusion Transformers (TFT), Deep-Q- Networks (DQN) to enhance the speed and performance. Multi-step prediction is applicable due to the strong forecasting capabilities. Such strong forecasting capabilities are ideal because they enable the DQN, which is a reinforcement learning algorithm, with real time trading decision making capabilities that are influenced by the ongoing volatility of the financial market. Our findings suggest that while the TFT component exhibits strong proficiency in predicting various market trends, the DQN is able to utilize these predictions to optimize decision-making within real-time trading activities. The hybrid D-TFT model achieves a Mean Absolute Percentage Error (MAPE) of 0.88%, significantly outperforming existing methods. This paper suggests a novel way of addressing additional challenges such as imprecision of predictive models, high uncertainties in stock market and improving the overall decision-making process. © 2025 IEEE.

关键词： Marketplaces

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Improving Text Summarization with Transformer Networks and Reinforcement learning for English 3

Improving Text Summarization with Transformer Networks and R...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Vijaya Durga, Chinni Bala Arunadevi, V. Pathak, Punit Prema, S. Nair, Hari Nagaraj, P. Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering & Technology A.P. Vijayawada India Dr.Mahalingam College of Engineering and Technology Department of Science and Humanities India Auro University Department of School of Liberal Arts and Human Sciences Surat India Panimalar Engineering College Department of English Chennai India Department of English and Foreign Languages Andhra Pradesh Bhimavaram India K.Ramakrishnan College of Engineering Department of Ai & Ml Tamil Nadu Trichy India

ISBN: (纸本)9798331528201

As the volume of text data continues to grow rapidly, efficient and accurate text summarization has become crucial for managing and processing this information. Traditional summarization methods, including extractive and abstractive approaches, face challenges in maintaining relevance, fluency, and factual consistency, particularly in complex domains such as legal documents. In this research, we propose an advanced summarization framework leveraging transformer networks, specifically designed to enhance the quality of English text summaries. The framework begins with the preprocessing of text data, including lowercasing, elimination of punctuation, HTML tags, stopwords, and stemming/lemmatization to standardize the input. Text feature extraction is then performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method, converting textual data into numerical representations that highlight the significance of terms within documents. The core of the framework employs a transformer model that eschews traditional RNNs in favor of multiple attention layers and positional encoding to capture global dependencies and enable parallel processing. The model's encoder-decoder structure uses self-attention mechanisms and multi-head attention layers to effectively encode the sequence and context of words, leading to high-quality abstractive summaries. The proposed architecture's effectiveness is evaluated using the BillSum dataset, which includes over 22,000 U.S. Congressional bills and human-written summaries. The results highlight the superior performance of the proposed Transformer model, which achieved the highest ROUGE-1, ROUGE-2, and ROUGE-L scores, with values of 37.03, 16.89, and 28.34, respectively and the Proposed method is implemented using python software. Our approach demonstrates significant improvements in summary accuracy and relevance compared to existing methods, making it a promising solution for automated summarization tasks across various domains.

关键词： Reinforcement learning

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Face ReID Method via Deep learning 23rd

Face ReID Method via Deep Learning

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Lima Romero, Yves Augusto da Rocha Neto, Ajalmar Rego IFCE Fed Inst Ceara Dept Comp Sci Fortaleza Ceara Brazil

ISBN: (纸本)9783031217524;9783031217531

Recenlty, many face reid strategies have been developped. These strategies create vectorial spaces capable of representing data on reduced dimensions. Such representations are produced by deep learning models that learn to maximize the intra-class similarity and minimize the inter-class similarity. The method described in this article proposes a new face reid strategy based on the Facenet model. It consists of training support vector machines on the euclidean distances calculated between the embeddings of the Facenet model, notably reducing the false positive rate.

关键词： Face ReID Machine learning Deep learning

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Detection of Brain Tumors from MRI Images using Novel Deep CNN 3

Detection of Brain Tumors from MRI Images using Novel Deep C...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Mohebbanaaz Deepthi Jordhana, P. Vamsi Krishna, G. Ravindra College of Engineering for Women Dept of ECE Andhra Pradesh Kurnool India PVKK Institute of Technology Dept of ECE Andhra Pradesh Anantapur India VNR Vignana Jyothi Institute of Engineering and Technology Department of EIE Hyderabad India

ISBN: (纸本)9798331523893

Brain tumors detection is the most crucial aspects in neuro-oncology. Early detection of tumors can significantly prevent critical health issues, improving the survival rates. Magnetic Resonance Imaging (MRI) has proven to be a effective tool in diagnosing brain tumors. This paper presents a deep learning (DL) methodology for the detection of brain tumors using Convolutional Neural Networks (CNNs). Given the complexity and high dimensionality of medical imaging data, transfer learning allows us to leverage pre-trained architecture as backbone and accelerate the process of training. The developed architecture is further is fine-tuned with different optimizing parameters. Our proposed approach achieved an accuracy of 98.70%. Experimental results demonstrate the success of developed models in detecting tumors with high accuracy, offering a promising solution for automated medical diagnostics. © 2025 IEEE.

关键词： Neuroimaging

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A Review on framework to Detect and Diagnose Body System based Cancer using deep learning Techniques 3

A Review on framework to Detect and Diagnose Body System bas...

引用

3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Gandikota, Hari Prasad Abirami, S. Annamalai University Department of Computer Science & Engineering Tamilnadu Chidambaram India

ISBN: (纸本)9798350399264

Cancer imaging examination is based mainly on doctors' manual interpretation, which requires professional abilities, clinical experience, and attentiveness. However, as the volume of medical imaging data grows, radiologists face increasing challenges. Artificial Intelligence (AI) detection of Digestive System Cancer (DSC) can provide a solution for automatic picture analysis and assist doctors in achieving high-precision intelligent cancer diagnosis. The primary purpose of this study is to explain the critical research methodologies for AI-based DSC detection, as well as to give meaningful learning and reference for relevant researchers. Meanwhile, it outlines the essential issues with these methodologies and provides better direction for future research. DSC automated categorization, recognition, and segmentation can be improved by utilizing machine learning and deep learning detection techniques, which reduce internal image information that humans find difficult to uncover. Using AI to assist imaging surgeons in diagnosing DSC for multiple organs and high incidence can achieve rapid and effective cancer identification while saving doctors' diagnosis time. These can be the foundation for better clinical diagnosis, therapeutic planning, and quantitative DSC evaluation. © 2023 IEEE.

关键词： Digestive system

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R2I: A Deep Fault Diagnosis Semantic Segmentation System Based on Knowledge Distillation in Photovoltaic Panels 3

R2I: A Deep Fault Diagnosis Semantic Segmentation System Bas...

引用

3rd international conference on Energy, Power and Electrical engineering, EPEE 2023

作者： Lei, Xiaoyanghao Ma, Kai Zhao, Junyi University of Liverpool Department of Electrical and Electronic Engineering Liverpool United Kingdom Xi'an Jiaotong-liverpool University Department of Computer Science Suzhou China

ISBN: (纸本)9798350318180

With the growing adoption of photovoltaic power systems, ensuring the reliable and efficient operation of photovoltaic panels is critical. However, defects such as cracks, dirt, and failures are common during long-term use. Manual inspection is subjective and resource intensive. Recent computer vision and machine learning techniques present new opportunities for automated diagnosis. This paper reviews photovoltaic panel defect detection using deep learning. Two main approaches exist: visible light and infrared imaging. Earlier works employed object detection with lower precision. Newer methods use semantic segmentation for pixel-level accuracy. However, most focus on single modalities and datasets. Transfer learning across diverse data could improve generalization. Few works estimate defect areas, needed for automated analysis. This paper proposes a dual-modality semantic segmentation approach with knowledge transfer learning. It shares representations across visible and infrared datasets via distillation. A lightweight network reduces parameters and computations for deployment. Soft targets provide latent features transferable to new data. An algorithm estimates defect area proportions to assess hazard levels. The multi-modal transferable learning system aims to advance automated, accurate photovoltaic panel defect diagnosis. Key innovations include tackling insufficient data via shared knowledge, reducing model complexity, improving generalization, and pioneering defect severity assessment. This can facilitate intelligent photovoltaic inspection to ensure system health, safety and longevity. © 2023 IEEE.

关键词： component Digital Power System Power System Fault Diagnosis Power System Security Smart Grid

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On Studying the Effect of data Quality on Classification Performances 23rd

On Studying the Effect of Data Quality on Classification Per...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Jouseau, Roxane Salva, Sebastien Samir, Chafik Univ Clermont Auvergne Clermont Ferrand France

ISBN: (纸本)9783031217524;9783031217531

During the last decade, data have played a key role for learning and decision making models. Unfortunately, the quality of data has been ignored or partially investigated as a pre-processing step. Motivated by applications in various fields, we propose to study data quality and its impact on the performance of several learning models. In this work, we first study the difficulty of repairing errors by introducing a list of elementary repairing tasks ranging from easy to complex with an increasing level. Then, we form categories from the state-of-the-art cleaning and repairing methods. We also investigate if it is always efficient to repair data. By including standard classifications models and public dataset, our work enables their use in different contexts and can be extended to other machine learning applications.

关键词： data quality data engineering data cleaning data repairing Classification Machine learning

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Basal Cell Carcinoma Prediction in Pigmented Skin Infection using intelligent Techniques 3

Basal Cell Carcinoma Prediction in Pigmented Skin Infection ...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Siva Prasad Reddy, K.V. Archana, K.S. Department of Computer Science and Engineering Pallavaram Chennai India

ISBN: (纸本)9781665462167

Melanoma is considered as a most lethal form of cancer. Design and development of computer-aided intelligent algorithms for early detection of skin cancer is the emerging research area. Despite many conventional mechanisms, a new type of cancer caused by unrepaired Deoxyribonucleic acid (DNA) within the skin cells. Due to its nature of rapid genetic mutations on the skin, it widely affects other body parts if not treated at early stages of intelligent computing evidenced the development of automated medical diagnosis and recommendation systems. It is possible to identify between melanoma and other classification of skin cancer based on the symmetry, color, size, form, and other characteristics of lesions. Numerous efforts are made by many researchers to develop various deep learning and machine learning inspired classification and segmentation algorithms to analyses skin lesion images. In existing the algorithm used for this research was naïve bayes, support vector machine etc. Here, after several methods such as data pre-processing, image segmentation, feature extraction and the feature extraction and the proposed algorithm of adaboost method, which is used to tune the algorithm to predict the skin infection. Finally, the proposed model has achieved 92.5% accuracy when compared with existing work. © 2023 IEEE.

关键词： Feature extraction

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium 3

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Gotte Ranjith Shubham Banerjee, Deepak School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns. © 2025 IEEE.

关键词： Nearest neighbor search

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intelligent learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers 23rd

Intelligent Learning Rate Distribution to Reduce Catastrophi...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Kenneweg, Philip Schulz, Alexander Schroeder, Sarah Hammer, Barbara Bielefeld Univ Inspirat 1 D-33615 Bielefeld Germany

ISBN: (纸本)9783031217524;9783031217531

Pretraining language models on large text corpora is a common practice in natural language processing. Fine-tuning of these models is then performed to achieve the best results on a variety of tasks. In this paper, we investigate the problem of catastrophic forgetting in transformer neural networks and question the common practice of fine-tuning with a flat learning rate for the entire network in this context. We perform a hyperparameter optimization process to find learning rate distributions that are better than a flat learning rate. We combine the learning rate distributions thus found and show that they generalize to better performance with respect to the problem of catastrophic forgetting. We validate these learning rate distributions with a variety of NLP benchmarks from the GLUE dataset. The source code is open-source and free software, available at https://***/TheMody/NAS-CatastrophicForgetting.

关键词： Natural language processing BERT learning rate Transformer

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