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.

Massive amounts of data, including big data, are generated and collected today from a variety of diverse data sources. These big data differ in terms of their veracity that ranges from imprecise and... Show More

Massive amounts of data, including big data, are generated and collected today from a variety of diverse data sources. These big data differ in terms of their veracity that ranges from imprecise and uncertain to precise. These data hide a huge amount of valuable information and precious knowledge that ought to be discovered. Examples of big data in the healthcare and epidemiological fields include information about patients afflicted with diseases such as Coronavirus disease 2019 (COVID-19). Researchers, epidemiologists, and policy makers get a great deal of help from the knowledge discovered from these data via data science techniques such as machine learning, data mining and online analytical processing (OLAP) in order to fully uncover the secrets of the disease. Eventually that may also inspire them to come up with ways to detect, control and fight the disease. In the article, the authors present a machine learning and big data analytical tool useful to process and analyze COVID-19 epidemiological data, while supporting big data visualization and visual analytics.

This study introduces a new meta-explainable machine learning methodology to enhance medical care recommendations and optimize healthcare operations through targeted interventions. It could assist a... Show More

This study introduces a new meta-explainable machine learning methodology to enhance medical care recommendations and optimize healthcare operations through targeted interventions. It could assist a large, and diverse population facing challenges in resource allocation and operational complexity. The proposed method utilizes a two-stage model. It first employs an Explainable Boosting Machine (EBM) and then provides the output from the initial phase to an unsupervised machine learning framework. It examines diverse aspects to identify the most critical set of features for focused operations and policy recommendations in designated areas. The research is based on data collected from three regions of India about maternal health and maternal mortality. The results highlight the accuracy of healthcare operations, thereby facilitating data-informed decisions. Implementing the method outlined in this paper in any other region across the globe will significantly enhance the design and execution of targeted healthcare initiatives, enhancing public health outcomes and optimizing resource.

The convergence of digital twin technology and data analytics continues in the area of smart cities focusing on the comprehensive study of data analysis and its visualization. It begins by standing... Show More

The convergence of digital twin technology and data analytics continues in the area of smart cities focusing on the comprehensive study of data analysis and its visualization. It begins by standing a foundational framework for data analytics and discussing the importance of these ideas in figuring out complex patterns, concluding, and supporting thoughtful decision-making. The article emphasizes the crucial role of data analytics for urban innovation in the context of smart cities using digital twins. The study delves into the complexities of data collection, integration challenges, and innovative solutions, underscoring the necessity of constructing a robust digital twin ecosystem with a variety of sensors and data sources with its visualization. Smart recommendations by monitoring, prescriptive, and real-time analytics are becoming essential tools for vigilant urban management for taking the best and next course of action. The article delves into predictive analytics, highlighting the synergy of data streams for a comprehensive understanding of urban dynamics.

Protection of networks from changing cyberthreats depends critically on intrusion detection. This article presents a hybrid deep learning framework using a tunicate swarm algorithm and brown-bear... Show More

Protection of networks from changing cyberthreats depends critically on intrusion detection. This article presents a hybrid deep learning framework using a tunicate swarm algorithm and brown-bear optimization for intrusion detection. The Tunicate Swarm Algorithm (TSA) was utilized for hyperparameter tuning; the Brown-Bear Optimization Algorithm (BBOA) was employed for feature selection, therefore lowering the dataset from 41 to 25 features. After five epochs, the model tested on the NSL-KDD dataset achieves 98% accuracy. Comparative study using conventional models showed that the suggested framework improved accuracy and loss reduction, therefore stressing its possibilities to improve intrusion detection systems.