9 research outputs found
Boosting Stock Price Prediction with Anticipated Macro Policy Changes
Prediction of stock prices plays a significant role in aiding the
decision-making of investors. Considering its importance, a growing literature
has emerged trying to forecast stock prices with improved accuracy. In this
study, we introduce an innovative approach for forecasting stock prices with
greater accuracy. We incorporate external economic environment-related
information along with stock prices. In our novel approach, we improve the
performance of stock price prediction by taking into account variations due to
future expected macroeconomic policy changes as investors adjust their current
behavior ahead of time based on expected future macroeconomic policy changes.
Furthermore, we incorporate macroeconomic variables along with historical stock
prices to make predictions. Results from this strongly support the inclusion of
future economic policy changes along with current macroeconomic information. We
confirm the supremacy of our method over the conventional approach using
several tree-based machine-learning algorithms. Results are strongly conclusive
across various machine learning models. Our preferred model outperforms the
conventional approach with an RMSE value of 1.61 compared to an RMSE value of
1.75 from the conventional approach
MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning
Mobile phones and other electronic gadgets or devices have aided in
collecting data without the need for data entry. This paper will specifically
focus on Mobile health data. Mobile health data use mobile devices to gather
clinical health data and track patient vitals in real-time. Our study is aimed
to give decisions for small or big sports teams on whether one athlete good fit
or not for a particular game with the compare several machine learning
algorithms to predict human behavior and health using the data collected from
mobile devices and sensors placed on patients. In this study, we have obtained
the dataset from a similar study done on mhealth. The dataset contains vital
signs recordings of ten volunteers from different backgrounds. They had to
perform several physical activities with a sensor placed on their bodies. Our
study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree,
Random Forest, and Logistic Regression) to analyze and predict human health
behavior. XGBoost performed better compared to the other machine learning
algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in
specificity, and 99.66% in F1 score. Our research indicated a promising future
in mhealth being used to predict human behavior and further research and
exploration need to be done for it to be available for commercial use
specifically in the sports industry.Comment: 6, Accepted by 2nd International Seminar on Machine Learning,
Optimization, and Data Science (ISMODE
Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images
Brain tumors pose a significant global health challenge due to their high
prevalence and mortality rates across all age groups. Detecting brain tumors at
an early stage is crucial for effective treatment and patient outcomes. This
study presents a comprehensive investigation into the use of Convolutional
Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance
Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy
individuals and patients with brain tumors, was processed and fed into the CNN
architecture. The SoftMax Fully Connected layer was employed to classify the
images, achieving an accuracy of 98%. To evaluate the CNN's performance, two
other classifiers, Radial Basis Function (RBF) and Decision Tree (DT), were
utilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The study
also introduced a clustering method for feature extraction, improving CNN's
accuracy. Sensitivity, Specificity, and Precision were employed alongside
accuracy to comprehensively evaluate the network's performance. Notably, the
SoftMax classifier demonstrated the highest accuracy among the categorizers,
achieving 99.52% accuracy on test data. The presented research contributes to
the growing field of deep learning in medical image analysis. The combination
of CNNs and MRI data offers a promising tool for accurately detecting brain
tumors, with potential implications for early diagnosis and improved patient
care
Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks
This study delves into the application of graph neural networks in the realm
of traffic forecasting, a crucial facet of intelligent transportation systems.
Accurate traffic predictions are vital for functions like trip planning,
traffic control, and vehicle routing in such systems. Three prominent GNN
architectures Graph Convolutional Networks (Graph Sample and Aggregation) and
Gated Graph Neural Networks are explored within the context of traffic
prediction. Each architecture's methodology is thoroughly examined, including
layer configurations, activation functions,and hyperparameters. The primary
goal is to minimize prediction errors, with GGNNs emerging as the most
effective choice among the three models. The research outlines outcomes for
each architecture, elucidating their predictive performance through root mean
squared error and mean absolute error (MAE). Hypothetical results reveal
intriguing insights: GCNs display an RMSE of 9.10 and an MAE of 8.00, while
GraphSAGE shows improvement with an RMSE of 8.3 and an MAE of 7.5. Gated Graph
Neural Networks (GGNNs) exhibit the lowest RMSE at 9.15 and an impressive MAE
of 7.1, positioning them as the frontrunner
Enhancing Traffic Density Detection and Synthesis through Topological Attributes and Generative Methods
This study investigates the utilization of Graph Neural Networks (GNNs) within the realm of traffic forecasting, a critical aspect of intelligent transportation systems. The accuracy of traffic predictions is pivotal for various applications, including trip planning, road traffic control, and vehicle routing. The research comprehensively explores three notable GNN architectures—Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs)—specifically in the context of traffic prediction. Each architecture's methodology is meticulously examined, encompassing layer configurations, activation functions, and hyperparameters. With the primary aim of minimizing prediction errors, the study identifies GGNNs as the most effective choice among the three models. The outcomes, presented in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), reveal intriguing insights. While GCNs exhibit an RMSE of 9.25 and an MAE of 8.2, GraphSAGE demonstrates improved performance with an RMSE of 8.5 and an MAE of 7.6. Gated Graph Neural Networks (GGNNs) emerge as the leading model, showcasing the lowest RMSE of 9.2 and an impressive MAE of 7.0. However, the study acknowledges the dynamic nature of these results, emphasizing their dependency on factors such as the dataset, graph structure, feature engineering, and hyperparameter tuning
Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks
Artistic style transfer, a captivating application of generative artificial
intelligence, involves fusing the content of one image with the artistic style
of another to create unique visual compositions. This paper presents a
comprehensive overview of a novel technique for style transfer using
Convolutional Neural Networks (CNNs). By leveraging deep image representations
learned by CNNs, we demonstrate how to separate and manipulate image content
and style, enabling the synthesis of high-quality images that combine content
and style in a harmonious manner. We describe the methodology, including
content and style representations, loss computation, and optimization, and
showcase experimental results highlighting the effectiveness and versatility of
the approach across different styles and contentComment: Incorrectly Inpu
Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks
Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content
Boosting Stock Price Prediction with Anticipated Macro Policy Changes
Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach
Retail Demand Forecasting: A Comparative Study for Multivariate Time Series
Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction models to gain a competitive edge. However, existing literature mostly focuses on historical sales data and ignores the vital influence of macroeconomic conditions on consumer spending behavior. In this study, we bridge this gap by enriching time series data of customer demand with macroeconomic variables, such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and unemployment rates. Leveraging this comprehensive dataset, we develop and compare various regression and machine learning models to predict retail demand accurately