Smart healthcare ecosystem for elderly patient care

© 2020 Croatian Society MIPRO. The healthcare sector is one of the rapidly growing service-based sectors in the world. Constant technological adoptions have been implemented to provide enhanced patient care and other healthcare-related services. Recently, the rapidly growing Internet and sensing technologies, cloud platforms, and remote healthcare monitoring have paved a way to build smart healthcare ecosystems. However, special attention needs to be given for delivering high-quality clinical services for the growing elderly population and critically ill patients who are finding it difficult to reach out for professional medical help either due to terminal illness or because of their remote geographical location. In this paper, we propose a service model for a smart healthcare ecosystem where the patient data is collected via medical IoT sensors connected to the patient, sensor\u27s data is stored in cloud infrastructure, and is analyzed by an expert from a remote telemedicine center. Moreover, an authorized telemedicine infrastructure\u27s person can regularly monitor the activities of the caregiver and interact with the patient without having the patient to visit the hospital. This proposed healthcare service model aims to improve trust, reliability, and cost-effectiveness of the overall healthcare service delivery

Learning context-aware outfit recommendation

With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching

An Intelligent Information System and Application for the Diagnosis and Analysis of COVID-19

The novel coronavirus spread across the world at the start of 2020. Millions of people are infected due to the COVID-19. At the start, the availability of corona test kits is challenging. Researchers analyzed the current situation and produced the COVID-19 detection system on X-ray scans. Artificial intelligence (AI) based systems produce better results in terms of COVID detection. Due to the overfitting issue, many AI-based models cannot produce the best results, directly impacting model performance. In this study, we also introduced the CNN-based technique for classifying normal, pneumonia, and COVID-19. In the proposed model, we used batch normalization to regularize the mode land achieve promising results for the three binary classes. The proposed model produces 96.56% accuracy for the classification for COVID-19 vs. Normal. Finally, we compared our model with other deep learning-based approaches and discovered that our approach outperformed

DL Multi-sensor information fusion service selective information scheme for improving the Internet of Things based user responses

Multi-sensor information fusion aids different services to meet the application requirements through independent and joint data assimilation. The role of multiple sensors in smart connected applications helps to improve their efficiency regardless of the users. However, the assimilation of different information is subject to resource and time constraints at the time of application response. This results in partial fulfillment of the application services, and hence, this article introduces a service selective information fusion processing (SSIFP) scheme. The proposed scheme identifies service-specific sensor information for satisfying the application service demands. The identification process is eased with deep recurrent learning in determining the level of sensor information fusion. This level identification reduces the unavailability of services (resource constraint) and delays in application services (time constraint). Through this identification, the applications\u27 precise demands are detected, and selective fusion is performed to mitigate the issues above. The proposed system\u27s performance is verified using the metrics delay, fusion rate, service loss, and backlogs

Real-time privacy preserving framework for Covid-19 contact tracing

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated with using such a technology are a serious cause of concern. There are growing concerns regarding the privacy of an individual\u27s location and personal identifiable information (PII) being shared with governments and/or health agencies. This study presents a real-time, trust-based contact-tracing framework that operates without the use of an individual\u27s PII, location sensing, or gathering GPS logs. The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range. The research validates the trust-based framework using Bluetooth as practical and privacy-aware. Using our proposed methodology, personal information, health logs, and location data will be secure and not abused. This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users

Secure Biomedical Document Protection Framework to Ensure Privacy Through Blockchain

In the recent health care era, biomedical documents play a crucial role, and they contain much evidence-based documentation associated with many stakeholders data. Protecting those confidential research documents is more difficult and effective, and a significant process in the medical-based research domain. Those bio-documentation related to health care and other relevant community-valued data are suggested by medical professionals and processed. Many traditional security mechanisms such as akteonline and Health Insurance Portability and Accountability Act (HIPAA) are used to protect the biomedical documents as they consider the problem of non-repudiation and data integrity related to the retrieval and storage of documents. Thus, there is a need for a comprehensive framework that improves protection in terms of cost and response time related to biomedical documents. In this research work, blockchain-based biomedical document protection framework (BBDPF) is proposed, which includes blockchain-based biomedical data protection (BBDP) and blockchain-based biomedical data retrieval (BBDR) algorithms. BBDP and BBDR algorithms provide consistency on the data to prevent data modification and interception of confidential data with proper data validation. Both the algorithms have strong cryptographic mechanisms to withstand post-quantum security risks, ensuring the integrity of biomedical document retrieval and non-deny of data retrieval transactions. In the performance analysis, Ethereum blockchain infrastructure is deployed BBDPF and smart contracts using Solidity language. In the performance analysis, request time and searching time are determined based on the number of request to ensure data integrity, non-repudiation, and smart contracts for the proposed hybrid model as it gets increased gradually. A modified prototype is built with a web-based interface to prove the concept and evaluate the proposed framework. The experimental results revealed that the proposed framework renders data integrity, non-repudiation, and support for smart contracts with Query Notary Service, MedRec, MedShare, and Medlock

A comprehensive review on medical diagnosis using machine learning

The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine learning could assist the doctors in making decisions on time, and could also be used as a second opinion or supporting tool. This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases. We present the various machine learning algorithms used over the years to diagnose various diseases. The results of this study show the distribution of machine learningmethods by medical disciplines. Based on our review, we present future research directions that could be used to conduct further research

An approach to forecast impact of Covid-19 using supervised machine learning model

The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix