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MultiLTR: Text Ranking with a Multi-Stage Learning-to-Rank Approach
Yang, Hua; Gonçalves, TeresaThe division of retrieval into multiple stages has evolved to balance efficiency and effectiveness among various ranking models. Faster but less accurate models are used to retrieve results from the entire corpus. Slower yet more precise models refine the ranking within the top candidate list. This study proposes a multi-stage learning-to-rank (MultiLTR) method. MultiLTR applies learning-to-rank techniques acro...
Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation
Rahman, Mizanur; Sarwar, Hasan; Kader, MD. Abdul; Gonçalves, Teresa; Ting Tin, TinThe average software company spends a huge amount of its revenue on Research and Development (R&D) for how to deliver software on time. Accurate software effort estimation is critical for successful project planning, resource allocation, and on-time delivery within budget for sustainable software development. However, both overestimation and underestimation can pose significant challenges, highlighting the need...
Improving Consumer Health Search with Field-Level Learning-to-Rank Techniques
Yang, Hua; Gonçalves, TeresaIn the area of consumer health search (CHS), there is an increasing concern about returning topically relevant and understandable health information to the user. Besides being used to rank topically relevant documents, Learning to Rank (LTR) has also been used to promote understandability ranking. Traditionally, features coming from different document fields are joined together, limiting the performance of stan...
Enhancing Biomedical Question Answering with Large Language Models
Yang, Hua; Li, Shilong; Gonçalves, TeresaIn the field of Information Retrieval, biomedical question answering is a specialized task that focuses on answering questions related to medical and healthcare domains. The goal is to provide accurate and relevant answers to the posed queries related to medical conditions, treatments, procedures, medications, and other healthcare-related topics. Well-designed models should efficiently retrieve relevant passage...
Im2Graph: A Weakly Supervised Approach for Generating Holistic Scene Graphs fro...
Ghosh, Swarnendu; Gonçalves, Teresa; Das, NibaranConceptual representations of images involving descriptions of entities and their relations are often represented using scene graphs. Such scene graphs can express relational concepts by using sets of triplets ⟨ — — ⟩. Instead of building dedicated models for scene graph generation, our model tends to extract the latent relational information implicitly encoded in image cap...
Review of Existing Datasets Used for Software Effort Estimation
Rahman, Mizanur; Gonçalves, Teresa; Sarwar, HasanThe Software Effort Estimation (SEE) tool calculates an estimate of the amount of work that will be necessary to effectively finish the project. Managers usually want to know how hard a new project will be ahead of time so they can divide their limited resources in a fair way. In fact, it is common to use effort datasets to train a prediction model that can predict how much work a project will take. To train a ...
Software Effort Estimation using Machine Learning Technique
Rahman, Mizanur; Roy, Partha Protim; Ali, Mohammad; Gonçalves, Teresa; Sarwar, HasanSoftware engineering effort estimation plays a significant role in managing project cost, quality, and time and creating software. Researchers have been paying close attention to software estimation during the past few decades, and a great amount of work has been done utilizing a variety of machine-learning techniques and algorithms. In order to better effectively evaluate predictions, this study recommends var...
Field features: The impact in learning to rank approaches
Yang, Hua; Gonçalves, TeresaLearning to Rank approaches employ Machine Learning techniques for Information Retrieval. Traditionally, the features needed to train a ranking model are naively combined after being extracted from the various fields of the texts. Nevertheless, if not considered carefully, the learning process can make use of strongly correlated features. Moreover, the learned ranking models are not, to date, systematically ana...
Segmented-Based and Segmented-Free Approach for COVID-19 Detection
Lasker, Asifuzzaman; Ghosh, Mridul; Das, Sahana; Obaidullah, Sk Md; Chakraborty, Chandan; Gonçalves, Teresa; Roy, KaushikAccording to WHO, lung infection is one of the most serious problems across the world, especially for children under five years old and older people over sixteen years old. In this study, we designed a deep learning-based model to aid medical practitioners in their diagnostic process. Here, U-Net based segmentation framework is considered to get the region of interest (ROI) of the lung area from the chest x-ray...
ant Disease Detection and Classification Using a Deep Learning-Based Framework
Ghosh, Mridul; Lasker, Asifuzzaman; Banerjee, Poushali; Manna, Anindita; Obaidullah, Sk Md; Gonçalves, Teresa; Roy, KaushikPlant diseases pose a significant threat to agriculture, causing substantial yield losses and economic damages worldwide. Traditional methods for detecting plant diseases are often time-consuming and require expert knowledge. In recent years, deep learning-based approaches have demonstrated great potential in the detection and classification of plant diseases. In this paper, we propose a Convolutional Neural Ne...