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Streamlining Financial Predictions: A Web application for Stock, Cryptocurrency, and Market Sentiment Analysis

Streamlining Financial Predictions: A Web Application for St...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Chettiyar Vani Vivekanand P. Kumar A. Amizhthini Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. the traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. this study will propose a web application that uses advanced time series models to try and overcome such challenges. the system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models; Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis; Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting; Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets; and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. this solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. the goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model.

关键词： Sentiment analysis Analytical models Social networking (online) Biological system modeling Time series analysis Predictive models Data models Real-time systems Cryptocurrency Forecasting

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Improving Clinical Decision-Making: Detection and Classification of Disease like Kidney Stone from CT Kidney Images Using Deep learning

Improving Clinical Decision-Making: Detection and Classifica...

引用

Intelligent Technologies (CONIT), international conference on

作者： Shivani Verma Pawan Kumar Singh Bhanu Sharma K.D Sharma Department of Electronics and Communication Engineering SRM University Sonipat India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Kidney stones are a common medical problem that causes a lot of people to go to the emergency department because they hurt so much and affect the quality of life. the use of several imaging modalities is essential in the diagnosis of kidney stone disease. the in-depth examination and analysis of these images call for the knowledge and experience of experts. When doctors need a helping hand making a diagnosis, they might turn to computer-aided diagnostic (CAD) techniques, which are practical ways. Recent advances in the application of deep learning methods to AI have prompted the authors of this work to suggest using these methods to automatically diagnose kidney stones by analyzing coronal computed tomography images. An evaluation of a deep learning model (DL) trained for classification tasks is provided in this paper. By examining training and validation data, the research determines the model’s efficacy. With a training and validation accuracy of 99% percent, the model excels at accurately classifying data instances. Contributing to a deeper understanding of the model’s capabilities, the study findings pave the path for its potential use in the world of healthy life.

关键词： Training Deep learning Solid modeling Accuracy Computational modeling Computed tomography Predictive models

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Research on application of Whale Optimization Algorithm in Financial Payment Fraud Detection

Research on Application of Whale Optimization Algorithm in F...

引用

Artificial Intelligence, Internet and Digital Economy (ICAID), international conference on

作者： Zepeng Shen Junyi Zhang Jiajia Chew Xingpeng Xiao Yaomin Zhang China-Britain Artificial Intelligence Association Oxford UK Electrical and Computer Engineering Lawrence Technological University Houston USA Accounting Universiti Sains Malaysia Penang Malaysia Computer Application Technology Shandong University of Science and Technology Qingdao China Computer Science University of San Francisco San Francisco USA

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

Due to the rise in fraudulent activity in financial payment systems, accurate and efficient methods of fraud detection have become paramount for financial security. To address the limitations of traditional machine learning methods on complex and high dimensional financial data, this research presents a Whale Optimization Algorithm (WOA) based financial payment fraud detection model to enhance the detection performance. On the one hand, in order to solve the convergence speed and local optimality of WOA, this paper proposes an adaptive weight adjustment strategy, which can dynamically adjust the movement strategy of individual whales according to the feedback in the search process, so as to avoid falling into local optimal and accelerate global search. Secondly, in order to enhance the stability of the algorithm, the simulated annealing mechanism is integrated with WOA to further optimize the global search ability. Finally, the adaptive feature selection method is integrated into the model, which is used to optimize the feature subset through WOA, retain the features highly related to fraudulent behavior, and reduce the interference and influence of redundant information on the model. Experimental results demonstrate that the improved WOA algorithm achieves higher detection rate and lower false positive rate compared to the traditional WOA and other commonly used algorithm in multiple real financial payment dataset fraud detection tasks.

关键词： Adaptation models Heuristic algorithms Simulated annealing Feature extraction Whale optimization algorithms Data models Stability analysis Fraud Classification algorithms thermal stability

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Sentiment Analysis of Indian Tourist Place Reviews: A machine learning-Based Exploration

Sentiment Analysis of Indian Tourist Place Reviews: A Machin...

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Intelligent Technologies (CONIT), international conference on

作者： B S Kiran Kumar M L Prajwal Nivedita Department of Computer Science School of Computing Mysuru Campus Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

the social media trend, where many followers are interested in visiting the areas based on the reviews offered by the influencers, has caused the tourism industry to start growing incredibly high in recent years. this study conducts sentiment analysis on user-generated reviews from online tourism review websites, employing advanced feature extraction techniques. the dataset which has been taken from different web sources contains textual reviews and numerical ratings, providing the framework for analysis. Data preprocessing includes text tokenization, stop word removal, lemmatization, and data cleaning, resulting in a refined dataset with a processed review column. For feature extraction, we used TF-IDF vectorization (limited to 5000 features) and GloVe-based word embedding for enhancing word importance and semantic relationships. We employ different machine learning models such as Naive Bayes, SVM, Logistic regression, and Random Forest Classifier (with 100 decision trees) for sentiment analysis, which is designed to handle complex data relationships. Random Forest has given the best accuracy of 89.54 percent compared to the other models, while future work will encompass precision, recall, and F1-score. Our novel approach integrates TF-IDF, word embedding, and Random Forest for improved sentiment analysis. this methodology results of subsequent discussion by a robust foundation for further exploration in this field.

关键词： Sentiment analysis Analytical models Accuracy Reviews Semantics Tourism industry Feature extraction

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Research on algorithm of DRC catalog generation based on machine learning 4

Research on algorithm of DRC catalog generation based on mac...

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2021 4th international conference on computer Information Science and application Technology, CISAT 2021

作者： Jiang, Yuan Yang, Wenhui Miao, Fang Ni, Yating Huang, Anqi School of Computer and Network Security Chengdu University of Technology Sichuan Chengdu610059 China Institute of Big Data Chengdu University Sichuan Chengdu610106 China

Aiming at the current logic and expansion issues in data governance caused by data scheduling in two directions: business retrieval and data processing. Data Oriented Architecture proposes a Registration Intermediate Library(RIL) and a Catalog Intermediate Library(CIL) to achieve separation of management and application. Among them, the business-oriented catalog intermediate library automatically classifies and labels data registration information through machine learning, and realizes the automatic generation of catalogs. there are many text classification methods based on machine learning, but the model obtained through unbalanced data set training often has performance degradation. In order to solve this problem, category weights are introduced in the feature vector to reduce the influence of most samples on the model parameters. Experiments on thUCNews text classification data set show that the proposed method can effectively improve the performance of baseline system, solve the problem of unbalanced training data categories and solve the problem of automatic generation of DRC directory. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： machine learning

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CulturalEcho and Educator App for Tourists

CulturalEcho and Educator App for Tourists

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Radhika V. Kulkarni Snehal Suryawanshi Aditya Shirsath Swati Sonone Siddhi Phapale Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Language barriers frequently stand in the way of successful communication in a society where languages are widely spoken. Moreover, the limited availability of educational resources on Indian history and culture hinders learning and cross-cultural exchange. the CulturalEcho AR Translator employs augmented reality (AR) technology to facilitate real-time translation of text written in Indian languages when viewed through a device’s camera. the system utilizes Optical Character Recognition (OCR) technology to identify the text in Indian languages and translates it into English. Users can quickly comprehend and interact with the content in English. Additionally, the application features a comprehensive content management system that includes a diverse collection of multimedia, historical, and cultural articles and images about India. the AR interface enables users to explore this educational archive, fostering interactive learning experiences enriched with cultural insights. the inclusion of a chatbot further enhances the experience by providing assistance and information, addressing user questions related to translations and cultural topics. the system employs authentication methods, including verified language databases sourced from reputable language authorities and government entities, to ensure the accuracy and legitimacy of translations. Additionally, it offers virtual tours of popular tourist attractions and historical landmarks to enhance the customer experience. With interactive 3D models and panoramas, users can explore historical sites and learn about their significance, deepening their understanding of Indian heritage and culture.

关键词： Translation three-dimensional displays Web services Optical character recognition Government machine learning Cultural differences Long short term memory Augmented reality application programming interfaces

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Predictive Analytics for Stroke Prevention: A machine learning Perspective

Predictive Analytics for Stroke Prevention: A Machine Learni...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Puranjay Savar Mattas Pradeep Kumar Mishra Himanshu Singh Dev Department of Computer Science & Applications School of Engineering & Technology Sharda University Gr. Noida Uttar Pradesh India

the objective of this study is to develop a machine learning model (ML) to obtain the risk of stroke in patients. We used a dataset of 4,981 patients to train and test the model. Decision Tree Classifier (DTC) was used as the ML model, which is a widely used algorithm for classification problems. Before training the model, we cleaned the dataset by removing inaccurate and incomplete data to ensure that the model is not fed with faulty data. this confirms that the model can make accurate predictions based on the available data. After cleaning the data, we trained the DTC model and tested it on the same dataset. Our model performance shows that this model can accurately predict the risk of stroke in patients with high accuracy. the outcome of this study can be beneficial for both patients and medical professionals. Patients can be informed of their risk of stroke and can take preventative measures to reduce their risk. In conclusion, we successfully developed an ML model using a Decision Tree Classifier to obtain the risk of stroke in patients. Our study demonstrates the potential of ML in healthcare and its application in predicting the occurrence of diseases. this article can serve as a foundation for future research on improving the accuracy of stroke risk prediction models, which can lead to better patient outcomes and improved healthcare practices.

关键词： Training machine learning algorithms Medical services machine learning Stroke (medical condition) Predictive models Prediction algorithms

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Monuments Identification using Satellite Images: A CNN based approach 22

Monuments Identification using Satellite Images: A CNN based...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Preet Jadhav Kirti Wanjale Abhijit Chitre Vedmani Vaidya Department of Computer Engineering Vishwakarma Institute of Information Technology India Department of Electronics and Telecommunication Vishwakarma Institute of Information Technology India Department of Mechanical Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781450399937

the significant rise in the amount of satellite images that have become available in recent years makes large-scale analysis of this data difficult. To make meaningful inferences from such images, one must have a thorough comprehension of the information they convey. Deep learning advances recently make it possible to train powerful machine learning models that can recognise several objects in a shot regardless of their attributes or distinct points of view. In this study, it is investigated if it's possible to identify monuments in satellite pictures using deep learning models. More specifically, the TensorFlow and Keras packages are used to build a model based on Indian monuments. Using Google Colab, the VGG16 model is trained on a variety of offline and online images. In order to enhance the performance of the model, the data augmentation technique is also used. Overall accuracy for VGG16 is 100 percent, which is extremely good. With the help of data augmentation on the model, a marginal improvement in accuracy is achieved. Lastly, this study's findings show that deep learning can be used to train a model, resulting in quite good outcomes even when trained on small and low-quality data sets.

关键词： TensorFlow Satellite Imagery Python Monument recognition Deep learning Keras Convolutional Neural Networks

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Design and application of VBA-based Intelligent Human Resources Report Generation Tool 23

Design and Application of VBA-based Intelligent Human Resour...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Haiqi Zhao Beijing Orient Institute of Measurement and Test China

ISBN: (纸本)9798400709517

this paper presents the requirements and objectives for building an intelligent human resources report generation tool. the tool utilizes Excel's built-in Visual Basic for applications (VBA) programming language, incorporating features such as form design, workbook management, and form control. It takes in human resources data sources, automatically performs statistical analysis on various human resources report data, and generates standardized Excel report files. this tool overcomes limitations posed by network environments and software/hardware infrastructure, enabling employees to swiftly and accurately complete report-related tasks. It simplifies data verification processes, reduces report management costs, and provides a solid data foundation for managerial decision-making at all levels. this further enhances the enterprise's information management and core competitiveness.

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Saudi Stock Market Prediction Using machine learning and Deep learning

Saudi Stock Market Prediction Using Machine Learning and Dee...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Raid Mohsen Alhazmi Computer Science University of Tabouk Tabouk KSA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

the research seeks to apply a particular approach to predicting a company's adjusted closing price in hopes of making an advancement in forecasting of the stock price. this research is significant due to its advantages to individuals, corporations, and governments. Approximating a stock's volatility is challenging for traders, and accurate predictions can help investors reduce portfolio risks. Stock prices do not behave in the linear manner. thus, we suggest the application of artificial intelligence techniques, namely multilayer perceptron (MLP) and long short-term memory (LSTM), to model the changes in Saudi Stock Exchange (Tadawul) index. this research aims at predicting the future of energy, telecommunication, financial, and industrial sectors of the stock exchange. the data was collected from Tadawul for the years 2018–2020 concerning these sectors. the AI algorithms aimed to predict the forthcoming benchmarks for the stock market sectors. the proposed models were tested against actual input data and simulation results, which produced extremely accurate results, demonstrating the effectiveness of the models. In addition, the correlation coefficient (R) of the MLP and LSTM models in predicting the values of Tadawul's four sectors integrated around 0.9950, signifying an exceptional correlation between forecasted and actual results. the forecasts were evaluated using four diverse trials, and between all the algorithms employed, LSTM produced the model's most precise predictions and best-fitting capacity.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Predictive models Prediction algorithms Data models Stock markets Forecasting Long short term memory

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