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Artificial Intelligence / Machine Vision / Computer Vision

Artificial Intelligence and Machine Vision: Neural Network-Based Image Processing Application

This postgraduate Artificial Intelligence and Machine Vision coursework requires students to design, implement and critically evaluate a neural network-based image-processing application addressing a real-world problem. Students may select an application such as medical imaging, plant or fruit classification, skin cancer detection, object detection or image segmentation and must demonstrate an appropriate end-to-end machine vision workflow. CN7023 Coursework T2 25-26 (1) The project begins with a clear definition of the selected real-world problem, the objectives of the proposed solution and its potential practical impact. Students are expected to demonstrate creativity in selecting and designing their approach, explain the neural-network or image-processing methods adopted and justify why the selected techniques are suitable for the chosen dataset and application. CN7023 Coursework T2 25-26 (1) A substantial component focuses on simulation and implementation. Students describe the dataset, including its source, size, classes and representative images, before explaining how image data were encoded and preprocessed for use within the chosen neural network. The report must then document the selected network architecture, learning algorithm and procedures used for training, validation and testing. CN7023 Coursework T2 25-26 (1) Model performance must be communicated using quantitative and visual evidence. Required outputs include test-set accuracy, accuracy curves across training, validation and testing, and a confusion matrix supported by appropriate explanation. Students must also critically analyse the results, identify factors affecting model performance and discuss alternative methods or simulation changes that could improve the solution. CN7023 Coursework T2 25-26 (1) The coursework permits several technical routes, including combining image processing with artificial neural networks, deep learning or computer vision, or focusing on one of these approaches independently. Development may be completed using MATLAB or Python. The wider module covers artificial neural networks, CNNs, digital image processing, image restoration, compression, segmentation, classification and ethical, legal, privacy and social issues associated with AI systems. CN7023 Coursework T2 25-26 (1) Module handbook 2526-B (1) Overview word count: approximately 335 words. Important note: the coursework cover page labels the assignment as “Individual Assignment 100%,” but the module handbook clarifies that the coursework report itself contributes 50% of the module, with the remaining marks allocated to MATLAB course completion (20%), lab tasks (15%) and presentation (15%). For the Reference Library, I would use the handbook’s 50% report weighting if you need to record the assessment contribution.

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Multimodal Sentiment Intelligence Platform for Dynamic Market Insights

This assignment presents the development of a Multimodal Sentiment Intelligence Platform for Dynamic Market Insights. The project addresses the need for real-time market sentiment analysis by combining text and visual data through an AI-driven multimodal approach. Traditional sentiment analysis methods may have limitations when dealing with diverse data modalities, and this project aims to address this gap by integrating multiple artificial intelligence and deep learning techniques. The primary objective is to develop an AI-driven platform capable of performing real-time sentiment analysis and supporting market trend prediction. The proposed system also aims to generate business intelligence insights that can support applications in marketing, finance, and customer service. The project incorporates deep learning, large language models, and multimodal fusion techniques to improve sentiment understanding. For text-based sentiment analysis, the assignment identifies BERT and GPT-2 for sentiment classification. For visual sentiment analysis, YOLOv8 is used for object detection while DeepFace is incorporated for facial emotion recognition. Feature-level and decision-level fusion strategies are applied to combine information from different modalities and improve the overall sentiment analysis process. Retrieval-Augmented Generation (RAG) is also incorporated to provide context-aware sentiment insights. The proposed platform uses Amazon reviews and IMDb reviews as text datasets. For image or video-based multimodal data, the assignment references the CMU-MOSEI dataset and an Amazon-Reddit merged reviews dataset. Overall, the work focuses on combining natural language processing, computer vision, deep learning, large language models, multimodal fusion, and retrieval-augmented generation to create a platform capable of producing dynamic sentiment and market insights.

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