Artificial intelligence and machine learning have been transforming the health care industry in many areas such as disease diagnosis with medical imaging, surgical robots, and maximizing hospital... Show More

Artificial intelligence and machine learning have been transforming the health care industry in many areas such as disease diagnosis with medical imaging, surgical robots, and maximizing hospital efficiency. The Healthcare service market utilizing Artificial Intelligence is expected to reach 45.2 billion U. S. Dollars by 2026 from its current valuation, off $4.9 billion. Diabetic Retinopathy (DR) is a disease that results from complications of type one and Type two diabetes and affects patients' eyes. Diabetic retinopathy, if remains unaddressed, is one of the most serious complications of diabetes, resulting in permanent blindness. The disease has been affecting the lives of 347 million people worldwide. The paper aims to propose a residual network-based deep learning framework for the detection of diabetic retinopathy. The accuracy of our approach is 83% whereas the precision value for checking the absence of DR is 95%.

The task of finding associative entities in knowledge graph (KG) is to provide a ranking list of entities according to their association degrees. However, many entities are not only linked in KG but... Show More

The task of finding associative entities in knowledge graph (KG) is to provide a ranking list of entities according to their association degrees. However, many entities are not only linked in KG but also associated in terms of user behaviors, which facilitates finding associative entities accurately. This manuscript incorporates KG with user-generated data to propose the Association Entity Graph Model (AEGM) to evaluate the association degrees. They first propose the joint weighting function to evaluate the entity associations and prove its submodularity theoretically as well as the greedy algorithm to select the candidates efficiently. They define the entity association information to score the entity association and give the hill climbing search based algorithm for AEGM construction. Following, they embed AEGM to calculate the association degrees and obtain the associative entities efficiently. Extensive experiments on three datasets show that the proposed method can achieve a better performance than some state-of-the-art competitors in accurately finding associative entities.

Error detection is an important part of preparing data for data analysis. Erroneous data can result in inaccurate analysis, resulting in garbage-in, garbage-out. Currently, many models utilize... Show More

Error detection is an important part of preparing data for data analysis. Erroneous data can result in inaccurate analysis, resulting in garbage-in, garbage-out. Currently, many models utilize either or both Qualitative and Quantitative methods to detect errors in the data. However, these methods are still limited in the errors they can detect. Hence FADE, Focused and Attention-based Detector of Errors, was proposed. FADE can detect errors within structured data with rows and columns. FADE utilizes the information found in surrounding cells within the same row to help determine if a cell is erroneous. It also learns the expected structure of the attributes in the dataset and the values expected in each attribute. This results in FADE having a much wider range of error type detection and having a higher classification of errors than other methods. FADE was evaluated and was found to detect these errors with relatively high performance.