A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction

The human liver is one of the major organs in the body and liver disease can cause many problems in human live. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict the liver disease. These techniques are improving day by day in order to predict and diagnose the liver disease in human. In this paper, real-world liver disease dataset is incorporated for diagnosing liver disease in human body. For this purpose, feature selection models are used to select a number of features that best are the most important feature to diagnose the liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naïve Bayes, supervised vector machine, decision tree, k near neighbor and logistic regression models to diagnose the liver disease in human body. The results are cross-validated by tenfold cross validation methods and achieve an accuracy as good as 93%.

Identifying Factors of Indian Health System and Their Influence for Providing Good Customer Care

Customer relationship management is important for any service industry as a satisfied customer is likely to remain loyal, spread publicity thereby ensuring profits to the organizations. Healthcare is an important and fast-growing service industry in which patient is the customer and maintaining good relationship with them is highly profitable. Good customer relationship comes from an understanding of patients’ expectations and what factors lead to patient satisfaction. WHO in its report in 2000 introduced the Concept-Responsiveness, which deals with ‘meeting the universal, legitimate expectations of the patients. This study identifies factors related to patients’ expectations, satisfaction and hence good customer relations in Indian health system. Structural equation modelling was used to measure the influence of the factors suggested. The results show significant influence of patient’s expectations on customer relationship.

Change Management Perceptions in Portuguese Hospital Institutions Through ITIL

Organizations function in complex, dynamic and unpredictable environments. Implementing changes must therefore be well planned, managed, and evaluated as such ongoing efforts link organizational performance to peer competitiveness and sustainability. In an era challenged with technological innovations, it is crucial to understand how new changes can leverage traditional methodologies and services supported by information and technology systems. As information-intensive organizations such as hospitals are highly dependent on changing information and technological systems, this understanding is key to evolve next generation hospitals. Specifically, this study analyzes how hospital managers in Portugal relate change to information systems’ management based on Information Technology Infrastructure Library methodology. The relationship between change and information technologies services is not sufficiently clarified and constitutes an excellent opportunity to increase knowledge in the field of information systems.

Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms

Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.

Dynamic contact network simulation model based on multi-agent system

Epidemic spread poses a new challenge to the public health community. Given its very rapid spread, public health decision makers are mobilized to fight and stop it by setting disposal several tools. This ongoing research aims to design and develop a new system based on Multi-Agent System, Suscpetible-Infected-Removed (SIR) model and Geographic Information System (GIS) for public health officials. The proposed system aimed to find out the real and responsible factors for the epidemic spread and explaining its emergence in human population. Moreover, it allows to monitor the disease spread in space and time and provides rapid early warning alert of disease outbreaks. In this paper, a multi-agent epidemic spread simulation system is proposed, discussed and implemented. Simulation result shows that the proposed multi-agent disease spread system performs well in reflecting the evolution of dynamic disease spread system's behavior

Food Intake Vision-Based Recognition System via Histogram of Oriented Gradients and Support Vector Machine for Persons With Alzheimer’s Disease

Due to cognitive decline, individuals with Alzheimer’s often suffer from malnutrition, forgetting to eat, even if food is presented. Therefore, assistance with feeding is needed. In this paper a vision-based system for monitoring of eating patterns is presented. Upper Body Region (UBR) is detected using Viola-Jones method, a histogram of oriented gradients (HOG) is generated for feature extraction, and a support vector machine (SVM) is used to distinguish eating versus non-eating. To reduce false positive results, Haar-like features are used to detect hands while moving between served food and mouth within the identified upper body region (UBR). A combined template image (CTI) method is proposed in this work to eliminate false positive hand detections where 30 hand eating posture images have been selected and combined into one template image. Matching implemented using CTI is 2.86 times faster than matching the subject to the 30 images separately. Experimental simulation used 33 videos of 163840 frames indicates that the proposed method achieves a high accuracy of 90.65%.

Adaptive Neuro-Fuzzy Inference Model for Monitoring Hypertension Risk

This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inference System (ANFIS). In order to develop the model cardiologists from teaching hospitals in Nigeria were interviewed so as to identify required variables for classification. Structured questionnaires were used to elicit information about the risk factors and the associated risk of hypertension from respondents. The MATLAB ANFIS Toolbox was used to simulate the model. The result of this study revealed that there were 33 main variables identified for monitoring hypertension risk and they were in line with the WHO/ISH classification standard. The result showed that majority of the patients selected had very high risk (57.0%) of hypertension which consisted more than 50% of the patients selected followed by 19% representing patients with high risk of hypertension, followed by patients with medium risk of hypertension. In conclusion, the model assist healthcare professionals to have accurate diagnosis, early detection and proper management of hypertension.

Factors Influencing the Adoption Intention of Blockchain and Internet-of-Things Technologies for Sustainable Blood Bank Management

Owing chiefly to the lack of suitable technology solutions, India is experiencing both shortage and wastage of blood units. In addressing such a challenge, we explore the unique role of Blockchain and Internet-of-things technologies in the overall blood supply chain management as an appropriate technology solution. Our study employs an integrated Task-Technology Fit and Technology Acceptance Model to empirically test and identify key factors influencing the adoption intention of the Blockchain and Internet-of-things enabled system. With the need to preserve donor and recipient data integrity and data privacy, the respective state and national health departments strictly regulate blood banks. Accordingly, our study also explores the role of government in supporting and overseeing security concerns in the future adoption of the Blockchain and Internet-of-things technologies. Finally, a solution based on the Blockchain and Internet-of-things technologies to ensure the sufficient availability of blood units at the national level is envisioned.

Critical Success Factors of Business Intelligence Implementation in Thai Hospitals

Business intelligence (BI) has become a popular among management executives of different industries. Many publications have mentioned Big Data and how to use data intelligently. However, little is known about how to successfully implement BI in the healthcare industry. The unique characteristic of this business, which focuses only on quality of care and patient safety, has a big impact on decision-making. This research is based on a literature review and empirical evidence collected from interviews with professionals involved in the healthcare industry. Twenty-four hospital executives and Information Technology staff who have direct or indirect experience with BI were interviewed. It investigates critical success factors for BI implementation in hospitals and provides insight into the healthcare industry in Thailand. The concept of grounded theory was applied for content analysis. Insights from this research contribute to academia and the healthcare industry by providing first-time evidence of specific factors for BI implementation and guidelines in hospitals.