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Facial expression recognition is a key topic in computer vision, playing a crucial role in non-verbal communication. With the rapid development of artificial intelligence, significant progress has been made in improving recognition accuracy and generalization abilities. Traditional methods often suffer from low precision and poor generalization, while deep learning models have substantially advanced the field. However, deploying deep and complex models on different platforms remains challenging due to their high computational demands and frameworks. Hence, developing an efficient, real-time, and lightweight facial expression recognition system is critical. This study focuses on creating an efficient, accurate, and lightweight real-time facial expression recognition system with an emphasis on cross-platform deployment. It integrates various deep learning and optimization techniques to demonstrate flexibility across platforms. In this context, this study evaluates the performance of 10 advanced CNN models (VGG16, VGG19, ResNet50, etc.) on the facial expression dataset FER2013. YOLOv8 combined with ResNet50 achieved 70.56% accuracy on FER2013, outperforming YOLOv8 alone by 2.1%. Multi-module fusion models (MobileFaceNet, IR50, HyViT, SE) achieved an accuracy of 92.58% and 74.8% on the RAF-DB and FER2013 datasets, respectively, showing superior performance in ablation experiments. Given the significant impact of data quality on model performance, this study performed data cleaning on the FER2013 dataset, resulting in a 3.25% accuracy improvement for the YOLOv8 + ResNet50 model, which reached 73.81%. The high-resolution RAF-DB dataset, with fewer errors, led to improved performance, achieving 92.56% accuracy with the fusion model. A multi-purpose facial expression recognition system, VISTA, was developed using Python and PyQt5. The system supports multiple data formats and provides real-time emotional feedback, thus enhancing its usability for both research and practical applications. Furthermore, the fusion model was quantized using the OpenVINO toolkit, reducing its parameters by 75% while maintaining an accuracy of 91.17%. Inference speed was improved, and XGrad-CAM was employed to enhance model interpretability, revealing that the YOLOv8 + ResNet50 combination more effectively captured facial features. Finally, the high-performance model was successfully deployed on Intel CPUs, NVIDIA GPUs, and embedded devices Raspberry Pi 4B, demonstrating the portability and flexibility of the VISTA system across various platforms. This research provides promising solutions for applications in human-computer interaction, affective computing, and real-time emotional analysis, with significant advancements made in improving system real-time performance, accuracy, and cross-platform deployment capabilities. It contributes to the development of facial expression recognition technology and lays the foundation for its widespread future applications in fields such as smart healthcare, business analytics, education, and mental health.
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Artificial Intelligence (AI) is being applied in different areas of Administration and Management including finance, e-commerce, etc. Project Management (PM) is one area that may benefit from the use of AI to support project managers in making more accurate predictions, more quickly, such as deadline adjustments and cost updates, while at the same time helping with some of repetitive tasks of PM by relieving managers from these processes. Nevertheless, multiple aspects are still in consideration to allow AI to be widely adopted in PM, including lack of validated systems, including aspects of quality and prevalence, trust from users, market and specialists, and how the government will play a role to support the wider adoption of AI tools. This research explores the integration of Artificial Intelligence (AI) in Project Management and its potential to enhance four aspects: service quality, trust, prevalence, and government support. The proposed methodology employs a systematic literature review (SLR) combining with a quantitative survey to assess the current state of AI in project management. The SLR covers scholarly articles from 2016 to 2021, focusing on AI's impact on project management across various industries. The survey, conducted among 200 professionals, gathers insights into AI's perceived benefits and challenges in project management. The research findings indicate a positive inclination towards AI in project management, with respondents recognizing its potential to improve efficiency, support data-driven decisions, and enhance risk management. However, the study also reveals concerns regarding data quality, privacy, and the need for ethical considerations in AI applications. Most respondents agree on the necessity of government support to foster AI adoption and the importance of establishing trust in AI systems through transparency and security measures. The thesis concludes with recommendations for practitioners and policymakers to effectively leverage AI in project management. It proposes a framework including the development of training programs, the establishment of quality standards for AI services, and the promotion of public-private partnerships to drive innovation. The study emphasizes the importance of a multi-faceted approach to AI integration, considering technological, organizational, and ethical dimensions.
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This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k-Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1-score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.
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