Emotion recognition from speech is an important aspect of human-computer interaction (HCI) systems, allowing machines to better understand human emotions and respond accordingly. This paper explores the use of machine learning techniques to recognize emotions in speech signals. We leverage the librosa library for feature extraction from audio files and train multiple machine learning models, including Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (k-NN), to classify speech emotions. The aim is to create an automated system capable of (...) identifying emotions like happy, sad, angry, neutral, and surprised from speech audio. (shrink)

The development of conversational agents that replicate the speech style of iconic characters from popular culture offers unique opportunities for both entertainment and artificial intelligence (AI) research. In this paper, we present the design, implementation, and evaluation of a Rick Sanchez Bot built using Transformer-based models, specifically the GPT-2 model. Rick Sanchez, a character from the animated series Rick and Morty, is known for his sarcastic, quick-witted, and often chaotic speech. The bot replicates Rick's unique dialogue style by utilizing a (...) pretrained GPT-2 model and fine-tuning it on character-specific dialogue data. We demonstrate the process of creating this conversational agent and explore how Transformer-based models can capture personality traits and speech patterns. (shrink)

Cognitive twins, digital replicas of cognitive processes, have emerged as a transformative approach in artificial intelligence and human-machine collaboration. This paper presents a framework for developing a cognitive twin by integrating a Distributed Cognitive System (DCS) with Evolutionary Strategies (ES). The DCS enables decentralized knowledge processing, while ES optimizes learning and adaptation over time. Our approach is evaluated on real-world datasets, demonstrating its efficiency in cognitive modeling and decision-making. Results highlight improvements in adaptability, scalability, and accuracy compared to traditional AI (...) models. (shrink)

Distributed Constraint Optimization Problems (DCOPs) provide a framework for solving multi-agent coordination tasks efficiently. However, their black-box nature often limits transparency and trust in decision-making processes. This paper explores methods to enhance interpretability in DCOPs, leveraging explainable AI (XAI) techniques. We introduce a novel approach incorporating heuristic explanations, constraint visualization, and modelagnostic methods to provide insights into DCOP solutions. Experimental results demonstrate that our method improves human understanding and debugging of DCOP solutions while maintaining solution quality.

Facial recognition is a computer vision task that involves identifying or verifying individuals based on their facial features. It has widespread applications in security, authentication, and human-computer interaction. Supervised learning techniques have become the foundation for facial recognition systems, as they enable the model to learn from labeled data to recognize patterns and make predictions. This paper explores the use of supervised learning algorithms, such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and k-Nearest Neighbors (k-NN), for facial recognition (...) tasks. We will demonstrate how these algorithms can be used to accurately identify individuals by learning from training datasets with labeled facial images. (shrink)

The early detection of cognitive concerns is crucial for timely intervention and improved patient outcomes. However, analyzing large-scale real-world data for cognitive decline presents significant challenges in efficiency and accuracy. This paper introduces an Autonomous AI Framework that leverages machine learning and natural language processing (NLP) to identify cognitive concerns from diverse datasets, including electronic health records (EHRs), social media interactions, and clinical notes. Our approach integrates deep learning models, feature selection techniques, and interpretability methods to enhance detection accuracy and (...) provide actionable insights. Experimental results demonstrate the framework’s effectiveness in identifying early signs of cognitive issues with high precision and recall. (shrink)

Breast cancer is one of the leading causes of death among women worldwide. Early detection plays a crucial role in improving survival rates, and machine learning (ML) provides powerful tools for identifying cancerous tumors in medical imaging and diagnostic data. This paper explores various machine learning techniques used for breast cancer detection, with a particular focus on the Wisconsin Breast Cancer Dataset (WBCD). We evaluate several classification models, including Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random (...) Forest (RF), for their ability to accurately classify benign and malignant tumors. The goal of this paper is to demonstrate the use of supervised learning techniques to enhance early breast cancer detection. (shrink)

Multi-agent reinforcement learning (MARL) has gained significant attention due to its applications in complex, interactive environments. Traditional MARL approaches often struggle with scalability and non-stationarity as the number of agents increases. Mean Field Reinforcement Learning (MFRL) provides a scalable alternative by approximating interactions using aggregated statistics. However, existing MFRL models fail to capture causal relationships between agent interactions, leading to suboptimal decision-making. In this work, we introduce Causal Mean Field Multi-Agent Reinforcement Learning (Causal-MFRL), which integrates causal inference techniques into the (...) mean field framework. By leveraging causal graphs and counterfactual reasoning, Causal-MFRL improves policy learning and enhances the interpretability of agent behaviors. We evaluate our approach on standard MARL benchmarks, demonstrating superior performance in efficiency, robustness, and generalization. (shrink)

Data cleaning and preprocessing are fundamental steps in the data analysis pipeline. These processes involve transforming raw data into a usable format by identifying and rectifying inconsistencies, errors, and missing values. Given the importance of data quality in achieving accurate and reliable analytical results, understanding the best practices for these stages is crucial. This paper outlines key techniques for data cleaning and preprocessing, including handling missing data, detecting and managing outliers, data normalization, encoding categorical variables, and dealing with noisy data. (...) Additionally, it explores the importance of these practices in ensuring robust and insightful analysis. (shrink)

Accurately predicting store sales is essential for businesses to optimize inventory management, marketing strategies, and staffing. Traditional sales prediction models often rely on historical data and simple linear trends, but these methods can be limited in capturing the complexity of factors that affect sales. This paper explores the application of machine learning (ML) algorithms to predict store sales, considering factors like promotions, holidays, weather conditions, and seasonal trends. We analyze various machine learning models, evaluate their performance, and demonstrate how they (...) can be used to improve sales forecasting and operational efficiency. (shrink)

Artificial Intelligence (AI) has made significant strides in mimicking human cognition, yet most AI systems remain rigid and lack human-like reasoning capabilities. Recent research suggests that embedding distinct personalities in AI can enhance its reasoning patterns, making interactions more natural and intuitive. This paper explores the impact of AI personalities on reasoning, discussing methodologies for implementation and evaluation. We present experimental results demonstrating how personality-infused AI improves decision-making, engagement, and adaptability across various tasks.