Journal of Big Data Technology and Business Analytics

Sentiment Analysis for Social Media Presence

2024-01-10T07:16:36+00:00

This paper introduces a robust Sentiment Analysis Solution tailored specifically for the dynamic landscape of social media presence. In an era where social media significantly influences personal and organizational reputations, understanding the sentiment expressed in posts, comments, and interactions becomes imperative. Leveraging advanced natural language processing and machine learning techniques, the system provides a comprehensive analysis of sentiments, offering valuable insights into public perception, customer feedback, and brand reputation. Unlike conventional methods, this solution prioritizes real-time analysis, enabling users to promptly gauge the overall sentiment trends. Overcoming challenges like language variations and emotional nuances, the project aims for high accuracy and precision, surpassing limitations often associated with rule-based approaches. Envisioning future enhancements, including multimodal analysis, advanced emotion recognition, and user-driven customization, distinguishes this solution from existing systems. Ethical considerations, continuous model training, and integration with Customer Relationship Management (CRM) systems are integral aspects of this project. These not only ensure responsible AI practices but also provide actionable insights for effective online reputation management. Empowering individuals and organizations, the project facilitates informed decision-making and proactive engagement based on evolving sentiment trends in the ever-evolving realm of social media.

Timely Identification of Parkinson's Disease

2024-01-17T08:26:26+00:00

This paper presents a novel approach aimed at the early identification of Parkinson's disease through the integration of Artificial Intelligence (AI) and analysis of spiral sketches. In the context of healthcare, where early diagnosis is paramount, leveraging AI to analyze spiral sketches provides a non-invasive and accessible method for identifying potential Parkinson's cases. In contrast to traditional diagnostic methods, which may lack sensitivity in the early stages of the disease, the system focuses on a user-friendly interface, allowing individuals to draw spirals. Advanced machine learning methodologies are utilized to conduct an extensive examination of these sketches, furnishing valuable insights into the probability of Parkinson's disease. Real-time analysis is prioritized in this solution, enabling prompt assessment of Parkinson’s symptoms in spiral sketches. The future vision includes enhancements such as multimodal analysis, telemedicine, integration, and longitudinal monitoring and progress tracking. Ethical considerations, regular model training and integration with the healthcare systems are important aspects of the system. These not only ensure responsible AI practices but also provide actionable insights for effective disease management. Empowering individuals and healthcare professionals, the project facilitates informed decision-making based on evolving trends in spiral sketch analysis in the realm of Parkinson's disease detection.

Emotion Detection from Audio: A Deep Learning Approach

2024-03-21T04:48:15+00:00

Human expressions and emotions play a vital role in Emotional Recognition. Universally, emotions can be often categorized and grouped into mere types like anger, happiness, sadness, fear, surprise, disgust and even neutral emotional state. Speech Emotion Recognition (SER) has been gaining increasing popularity in the growing field of Human–Computer Interactions (HCIs). The Speech Emotion Recognition (SER) system is used to extract and predict the emotional tone of a speaker through audio signals. Emotion detection is one of several things that can be inferred from an audio signal, including age, gender, person, and action. A difficult issue in audio signal processing is emotion recognition from speech or audio. Human speech is recognised for carrying linguistic information in addition to the identity and feelings of the speaker. Emotional recognition from speech or audio is a challenging problem in audio signal processing. This proposed method deals with the Emotion detection from Audio Speech using CNN and LSTM methods. This method of emotion recognition is based on the spectrograms and mel-spectrograms that are returned from short voice recordings. An adequate model was required to learn and analyse sequential data, such as audio files. In this proposed method, a long short-term memory Recurrent Neural Network (RNN) model is utilised. The primary goals are to create a system capable of identifying speech-embedded emotions and to make accurate predictions.

Forecasting Precision: The Role of Graph Neural Networks and Dynamic GNNs in Weather Prediction

2024-04-11T12:03:48+00:00

Weather forecasting is essential for addressing global climate change concerns, relying on the analysis of multivariate data from diverse meteorological sensors. These sensors include ground-based, radiosonde, and satellite-mounted sensors, providing a comprehensive understanding of atmospheric conditions. To analyze this data effectively, a weather forecasting model based on Graph Neural Networks (GNNs) is employed. GNNs, built on graph-based learning principles, have shown significant empirical efficacy in various machine learning paradigms. In this study, we evaluate the weather forecasting performance of GNNs in comparison to traditional machine learning models. By leveraging the capabilities of GNNs, which excel in capturing complex relationships within data, we aim to enhance the accuracy and reliability of weather predictions. This research contributes to advancing the field of weather forecasting by exploring novel methodologies that can potentially improve prediction accuracy and provide more robust insights into climate dynamics. Additionally, by assessing the performance of GNNs against conventional models, we gain valuable insights into the strengths and limitations of different forecasting approaches. Ultimately, the findings of this study have implications for improving weather forecasting techniques, which are crucial for mitigating the impacts of climate change and enhancing resilience to extreme weather events on a global scale.

Stock Price Prediction and Pattern Detection Using Deep Learning

2024-04-16T06:18:07+00:00

Accurately predicting stock market movements remains a significant challenge, yet one that continues to attract intense interest. This project leverages data mining and warehousing techniques to explore historical stock price data, aiming to uncover recurring patterns, forecast potential future trends, and evaluate the consistency of a specific stock's behaviour relative to its identified patterns. By employing various data mining algorithms and implementing effective warehousing strategies, the project seeks to extract valuable insights that can inform investment decisions and potentially contribute to superior market performance. But among these goals are some serious obstacles. Accurate prediction is hampered by the noise and uncertainties that are present in stock market data. Furthermore, fine-tuning and experimentation are necessary to achieve optimal results when optimizing the hyperparameters of long short-term memory networks (LSTM networks). Moreover, strong preprocessing methods are required to handle datasets that contain inconsistent or missing data. Lastly, proficient data visualization and user interface design abilities are required when creating an educational and engaging dashboard with Plotly Dash.