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Recurrent Neural for Time Series Prediction in Finance 2

Recurrent Neural for Time Series Prediction in Finance

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Name of Organization Nitte (Deemed to Be University) Mangalore India

ISBN: (纸本)9798350355468

Overcoming risk is crucial when predicting financial time series, such as stock prices, due to market volatility influenced by factors like economic reports, political events, and investor sentiment. Linear models like ARIMA handle linear dependencies well but fail to capture nonlinear relationships, which are key in financial forecasting. While Long Short-Term Memory (LSTM) models excel at capturing temporal dependencies, they struggle with identifying significant market events, potentially contaminating predictions. This paper introduces an Attention RNN with Sentiment Integration, which combines Recurrent Neural Networks (RNN) with an attention mechanism for sentiment analysis of financial news and social media. The model considers relevant time steps and market sentiment, improving context-awareness. Results demonstrate that this hybrid approach outperforms traditional methods by providing a more accurate and flexible forecasting model, addressing the complexities of financial markets. Additionally, sentiment data helps the model capture both stock-specific and overall market sentiment, particularly during periods of heightened uncertainty or volatility, offering a more comprehensive approach to stock price prediction. © 2025 IEEE.

关键词： Attention Mechanism Financial Time Series Prediction LSTM Machine Learning Recurrent Neural Networks Sentiment Analysis

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Predicting Financial Market Movements Through Time Series Analysis and Advanced Machine Learning Algorithms

Predicting Financial Market Movements Through Time Series An...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Amarnath Reddy, B. Kathiriya, Harsh Thapliyal, Nitin Vasuki, S. Sachdeva, Meena Shobana, A. Vignana Jyothi Institute Of Management Department Of Finance & Marketing Hyderabad India Big Analytixs AtlantaGA United States Graphic Era Hill University Department Of Computer Science And Engineering Dehradun India Velammal College Of Engineering And Technology Department Of Ece Madurai India Jims Engineering Management And Technical Campus Department Of Computer Science Uttar Pradesh Greater Noida India Sri Krishna Arts And Science College Department Of Computer Applications Coimbatore India

ISBN: (纸本)9798350371567

A novel approach for forecasting the movements of financial markets is presented in this research. It combines Auto Regressive Integrated Moving Average (ARIMA) time series analysis with Random Forest machine learning techniques. Improving predicting accuracy in ever-changing market conditions is the primary goal of the study. Our suggested model achieves better results than current models using a sample dataset. It achieves an MAE of 1.6, an MSE of 2.8, and an RMSE of 1.67. The R-squared value is 0.797, which means that the model explains a significant amount of the variation. This combined methodology takes advantage of ARIMA and Random Forest's complimentary qualities to identify patterns in financial data, whether they are linear or nonlinear. This research adds to the growing body of knowledge that combines machine learning with time series analysis to provide a powerful resource for those making important financial decisions. To further improve the model's flexibility and utility across varied market situations, future research should focus on parameter tweaking, researching other ensembles, and conducting real-world implementation. Finally, this research establishes the groundwork for a novel hybrid model that might greatly enhance the precision of financial market forecasts in the future. © 2024 IEEE.

关键词： Decision making

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Comparative Study of Machine Learning Techniques on Chronic Kidney Disease Prediction 3

Comparative Study of Machine Learning Techniques on Chronic ...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Panigrahi, Soumyarashmi Adhikary, Dibya Ranjan Das Pattanayak, Binod Kumar Mohanty, Bibhuprasad Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Siksha 'o' Anusandhan Deemed to Be University Department of Computer Science & Engineering Bhubaneswar India Siksha 'o' Anusandhan Deemed to Be University Department of Electronics & Communication Engineering Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350303360

A malfunctioning kidney can cause the increase of waste in the blood, adversely affecting various bodily processes and systems. ML techniques can assist in classifying patients as having chronic kidney disease (CKD) or not. Although previous researchers have conducted several studies, they have often neglected important steps like data preprocessing and feature engineering. In this paper, we have addressed these gaps by implementing various data preprocessing and feature engineering techniques such as Principal Component Analysis (PCA) on the CKD dataset. By carefully selecting features from our dataset, we identified the top-performing classification models which are RFC, SVC (Polynomial Kernel), XG Boost Classifier (XGBC) and Gradient Boost Classifier (GBC) gave the best performance in terms of accuracy. To evaluate the effectiveness of these models, we have utilized various performance metrics. The results obtained have significant contribution to the understanding and management of CKD, ultimately helping in the early detection and treatment of this widespread and critical health issue. © 2023 IEEE.

关键词： Principal component analysis

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Exploring Reinforcement Learning Algorithms for Autonomous Agents in Dynamic Environments

Exploring Reinforcement Learning Algorithms for Autonomous A...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Kumar, S. Magesh Swaminathan, Subbiah Sugumaran, D. Saranya, N. Naga Ashokkumar, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Vel Nagar Tamil Nadu Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350388602

Reinforcement Learning (RL) algorithms are widely considered to be the enabling technology for deep learning and allow for agents in complex systems to learn in a dynamic environment. In order to present in this paper a break-through training methodology for learning RL agents in the dynamic environment, advised by the following adaptability, robustness, and scalability purposes. Our approach is assessed in an array of the model environments which are open to the users. This framework is labelled as OpenAI Gym environment. We apply different reinforcement learning algorithms in our experiments among which Covering Concurrent and Differentiable in Q-learning, Meta-learning (MAML), Virtual Imitation and Q-learning, and Dyna-Q are the most popular. Meta-learning's performance highlights its power and efficiency in grasping and optimizing decision-making logic in dynamic situations within 20 epochs and reaching the cumulative reward of 500. As a matter of fact, adaptive search strategies, for instance, UCB are another tool that is extremely useful reaching to 490 at 25 epochs after all. Transfer learning techniques like the Progressive Neural Networks shows extremely high power of learning in collecting a reward of 500 and in less than 20 epochs it also converges. Moreover, scalable parallel training methods such as CPDG which are normalized in distribution, have huge scalability and robustness can be applied for large scale dynamic environments. Consequently, it is concluded that the presented approach was very good in giving RL agents the ability to navigate environments and succeed in dynamic settings, thus contributing to the development of autonomous systems not only in the area of RL but also everywhere else. © 2024 IEEE.

关键词： Adversarial machine learning

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Resolving Blockchain's Immutable Dilemma

Resolving Blockchain's Immutable Dilemma

引用

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Amritesh Kumar Monu Nagar Lokendra Vishwakarma Debasis Das IDRD: Centre for AIoT & Applications Indian Institute of Technology Jodhpur India Department of Computer Science & Engineering Indian Institute of Technology Jodhpur India Department of Computer Science & Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331509347

ISBN: (纸本)9798331509354

Redactable blockchain presents a solution for fraudulent transaction modification and storage overload by allowing limited data manipulation using Chameleon Hash Functions (CHF). However, CHF-based redactable blockchain encounters significant security challenges like misuse of edit rights, node desynchronization, higher transaction verification, and redaction latency. To solve these challenges, we propose SERB, a secure and efficient redactable blockchain protocol. SERB utilizes a Node Selection, Redactable Consensus, and novel Parameterized Chameleon Hash Function (pCHF) for proper use of edit rights and node synchronization with minimization of transaction verification and redaction latency. The security analysis shows that SERB is secured against tampering attacks, collusion attacks, and forged attacks. The performance analysis indicates that the SERB reduces transaction verification latency by 20% and minimizes redaction latency, such as transaction modification by 25 % and block deletion by $\mathbf{5 0 \%}$ compared to state-of-the-art approaches.

关键词： Hash functions Cloud computing Protocols Minimization Blockchains Performance analysis Security Synchronization

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Bharatanatyam hasta mudra categorization using deep learning approaches 2

Bharatanatyam hasta mudra categorization using deep learning...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Nandeppanavar, Anupama S. Kallur, Shanta S. Thotad, Puneeth Sankannavar, Vaishnavi A. K. L. E. Institute of Technology Department of Master of Computer Applications Hubballi India Affiliated to Visvesvaraya Technological University Belagavi India K. L. E. Institute of Technology Department of Computer Science and Engineering Hubballi India K. L. E. Institute of Technology Vtu Department of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350314045

The present study explores the precise identification of Bharatanatyam hasta mudras (hand gestures) by applying advanced convolutional neural networks and deep learning methodologies. Specifically, the article employs the Convolutional Sequential Neural Network, VGG-19, and, prominently, ResNet50V2 architectures to analyze a meticulously labeled dataset comprising Bharatanatyam hasta mudra images. With accuracy, recall, precision, and F1-score, key performance metrics are systematically employed for comprehensive performance evaluation, revealing an impressive accuracy rate of approximately 96.44%. Notably, the projected system is a valuable resource for Bharatanatyam practitioners, offering a sophisticated tool for discovering and mastering diverse hasta mudras. Its potential impact extends to dance schools and academies, where the system can be instrumental in teaching and integrating various hasta mudras into choreographic works. Beyond dance education, the article's contribution resonates in the broader context of preserving and promoting Indian classical dance forms. © 2023 IEEE.

关键词： Convolutional neural networks

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A Machine Learning based Facial Expression and Emotion Recognition for Human computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the Vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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Leveraging XGBoost for Predictive Analytics in Healthcare: Enhancing Disease Diagnosis 7

Leveraging XGBoost for Predictive Analytics in Healthcare: E...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Kotiyal, Arnav Habelalmateen, Mohammed I. Rana, Ajay Devi, V.S Anusuya Rao, Bolleddu Devananda Bansal, Saloni Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Amity University Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Departyment of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350350067

In this study, we look at how XGBoost (Extreme Gradient Boosting) may be used in healthcare predictive analytics to improve the speed and precision of illness detection. Thanks to its built-in regularisation algorithms that avoid overfitting and its capacity to handle big datasets, XGBoost has proved useful in a broad variety of prediction applications. It is a scalable and high-performance machine learning algorithm. In order to improve patient outcomes, healthcare providers must ensure that patients are diagnosed with chronic and life-threatening conditions including cancer, heart disease, and diabetes as soon as possible. To test how well XGBoost performs in comparison to more conventional machine learning techniques, the research makes use of healthcare datasets collected from the actual world. The effectiveness of the model in predicting illness outcomes is evaluated using critical metrics including accuracy, precision, recall, and F1-score. Based on the findings, XGBoost is the best classifier for real-time healthcare applications because to its quicker processing efficiency and greater predictive ability. Additionally, the article delves into the model's interpretability and how it may be integrated into clinical decision support systems. By demonstrating how XGBoost may improve diagnostic accuracy and bolster individualised treatment plans, this study adds to the expanding area of AI-driven healthcare. © 2024 IEEE.

关键词： Predictive analytics

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Drug Discovery with Machine Learning: Target Identification using Random Forest

Drug Discovery with Machine Learning: Target Identification ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Choudhari, Pragati Rawat, Ruchira Al-Fatlawy, Ramy Riad Shrivastava, Anurag Yadavk, Kanchan Pratap, Arun School Of Computer Science Engg. & Applications D Y Patil International University Pune Akurdi India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Gla University Department Of Electrical Engineering Mathura India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

The abstract presents a see into our inquiry about on-target distinguishing proof in sedate disclosure utilizing Random Forest, displaying the transformative effect of machine learning. Leveraging different natural datasets, we utilized Random Forest nearby other calculations to foresee potential medicate targets. Our comprehensive assessment measurements, counting accuracy, precision, recall, and F1 score, emphasize the prevalent execution of Random Forest in comparison to Decision Trees, Support Vector Machines, and k-Nearest Neighbors. The results demonstrate a precision of 85%, accuracy of 86%, review of 84%, and an F1 score of 85% for Irregular Forest, certifying its viability in precisely recognizing promising sedate targets. The study contributes to the advancing scene of machine learning applications in biomedical research, emphasizing the potential of gathering learning procedures, especially Irregular Woodland, to streamline and improve the target identification stage of sedate improvement. This research adjusts with broader patterns in healthcare and pharmaceutical examinations, highlighting the flexibility of machine learning in tending to complex challenges and quickening the disclosure of novel restorative arrangements. © 2024 IEEE.

关键词： Decision trees

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Content Based Image Retrieval: A Study of Approaches and Techniques

Content Based Image Retrieval: A Study of Approaches and Tec...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Mehnaz Bano Priya Matta Shivali Chandel Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Applications Tula's Institute Dehradun India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The Content Based Image Retrieval (CBIR) is basically used to extract images from the collection of images using several image matching and extraction methods. As it is now with the advancement and ongoing consistent technology and negligible cost of the electronic, digital and virtual sensors, a huge collection of image databases is processed by different types of applications. This type of database of images extends the requirement that we should provide effective search methods for the retrieval of images that can fulfill the demands of end-user. The texture, color, or shape of a picture is the most basic feature used to symbolize and index the images. These distinct elements of photographs are retrieved and used as a measure to find image similarities. A search procedure is initiated for relevant images that fulfill the user's information need using a sketch drawing or a query image given by the user that has similar features. The relevance feedback can be used as an optional step to retrieve and enhance the CBIR process. In this paper, after extracting semantic characteristics from photos, an analysis of the content of the images is performed with respect to features connected to the low level. In this paper, an overview of traditional CBIR methods (e.g.: Color histogram-based, texture based and shape based) are discussed. This paper's primary goal is to study the comprehensive details on the various computational techniques used for content-based image retrieval. To further enhance the efficiency of the image retrieval process, their limitations are also addressed.

关键词： Costs Shape Image color analysis Soft sensors Search methods Image matching Image retrieval Semantics Feature extraction Indexes

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