Enhancing Multimodal Information Retrieval Through Integrating Data Mining and Deep Learning Techniques

Multimodal information retrieval, the task of re trieving relevant information from heterogeneous data sources such as text, images, and videos, has gained significant attention in recent years due to the proliferation of multimedia content on the internet. This paper proposes an approach to enhance multimodal information retrieval by integrating data mining and deep learning techniques. Traditional information retrieval systems often struggle to effectively handle multimodal data due to the inherent complexity and diversity of such data sources. In this study, we leverage data mining techniques to preprocess and structure multimodal data efficiently. Data mining methods enable us to extract valuable patterns, relationships, and features from different modalities, providing a solid foundation for sub- sequent retrieval tasks. To further enhance the performance of multimodal information retrieval, deep learning techniques are employed. Deep neural networks have demonstrated their effectiveness in various multimedia tasks, including image recognition, natural language processing, and video analysis. By integrating deep learning models into our retrieval framework, we aim to capture complex intermodal dependencies and semantically rich representations, enabling more accurate and context-aware retrieval

An Efficient CBIR System for Medical Images Using Neural Network

This paper introduces an innovative Content-Based Image Retrieval (CBIR) system that has been specifically developed for medical databases. Its objective is to resolve the drawbacks of conventional keyword-based search approaches when considering the widespread digitization of medical illustrations, diagrams, and paintings. In contrast to conventional methods that rely on textual queries, CBIR systems effectively locate and retrieve relevant images by analyzing image content using computer vision and image processing techniques, as well as information retrieval and database methods.A key challenge in CBIR lies in bridging the semantic gap between high-level user queries, often expressed through example images, and the low-level features of images such as texture, shape, and objects. This paper explores techniques to mitigate this disparity, enhancing the system's ability to accurately interpret user queries and retrieve relevant images. The proposed CBIR system operates within a medical database containing images of various human organs, including the brain, heart, hand, chest, spine, and shoulder, categorized into six distinct classes. By leveraging low-level image features such as texture and shape, extracted using methods like mean, variance, standard deviation, area, perimeter, circularity, and aspect ratio analysis, the system performs iterative searches to retrieve relevant images.Classification of retrieved images is accomplished using Artificial Neural Networks (ANN), which have demonstrated efficacy in medical image classification tasks based on imaging modalities and the presence of normal or abnormal conditions. Performance evaluation of the CBIR system is conducted using confusion matrices to calculate precision and recall, essential metrics for assessing retrieval accuracy. By focusing on medical datasets and integrating advanced feature extraction and classification techniques, this CBIR system aims to significantly enhance image retrieval efficiency and accuracy, particularly in the context of medical applications where precise retrieval of relevant images is critical for diagnostic and research purposes. &nbsp

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dirichlet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polya-urn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.Comment: Full version of ICML 201

Interactive multimodal narrative as an approach to developing emergent literacy in early childhood education

At present, the Portuguese education system promotes the development of new literacies in all education levels in response to new requirements of the digital world which imply changes in children’s education. In fact, reading and writing are no longer limited exclusively to books, rather they are associated with diversified digital media integrating text, sound, image, and video. In this context, we developed an exploratory study in a kindergarten with the children and their family members. Data were collected through participant observation, children conversational interviews, and digital narratives produced by children. Data analysis is based on content analysis with NVivo software support. The emergence of literacy is evident in verbal interactions in peer-group work, in contact with the written text, in “writing” in various media, and in sharing knowledge, discoveries, and digital narratives produced in an online community http://janelajardim.ning.com). This paper presents: (1) a systematic literature review in the field of digital narrative and emergent literacy; (2) the description of children’s activities concerning interactive multimodal narrative; (3) the results of this work; and (4) the conclusion about contribution of multimodal narratives to the emergence of reading, writing, and digital skills

E-methods in literary production: integrating e-learning in creative writing

This paper discusses the integration of e-learning in creative writing. The online approach to the teaching of creative writing takes into account today’s Malaysian youth and their fascination with computer technology. It is this appeal of innovation in electronics and knowledge that leads an educator to design an on-line approach to a creative writing course. The theoretical construct used to support the discussion is Anderson’s theory that on-line learning is knowledge-, community-, assessment-, and learner-centered. The writer, who is also the course developer, analyses a poetry-writing activity, which students undertake, and the e-portfolio used in the course. To analyze the processes involved in this creative writing exercise Macherey’s (1978) Theory of Literary Production is adapted and utilized. This theory, which regards literary production as a process imitating that of a production line, provides the methodology and conceptual framework for analyzing the raw materials collected by the students and their transformation during the writing process. This paper thus addresses the benefits of e-learning in a creative writing context

Integrating Authentic Digital Resources in Support of Deep, Meaningful Learning

"Integrating Authentic Digital Resources in Support of Deep, Meaningful Learning," a white paper prepared for the Smithsonian by Interactive Educational Systems Design Inc., describes instructional approaches that apply to successful teaching with the Smithsonian Learning Lab.After defining its use of terms such as deeper learning and authentic resources the authors review the research basis of three broad approaches that support integrating digital resources into the classroom:Project-based learningGuided exploration of concepts and principlesGuided development of academic skillsThese approaches find practical application in the last section of the paper, which includes seven case studies. Examples range from first-grade science, to middle-school English (including ELL strategy) to a high-school American government class. In each example, students study and analyze digital resources, going on to apply their knowledge and deepen their understanding of a range of topics and problems

Integration of Exploration and Search: A Case Study of the M3 Model

International audienceEffective support for multimedia analytics applications requires exploration and search to be integrated seamlessly into a single interaction model. Media metadata can be seen as defining a multidimensional media space, casting multimedia analytics tasks as exploration, manipulation and augmentation of that space. We present an initial case study of integrating exploration and search within this multidimensional media space. We extend the M3 model, initially proposed as a pure exploration tool, and show that it can be elegantly extended to allow searching within an exploration context and exploring within a search context. We then evaluate the suitability of relational database management systems, as representatives of today’s data management technologies, for implementing the extended M3 model. Based on our results, we finally propose some research directions for scalability of multimedia analytics