/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Medical Image Analysis using Convolutional Neural Networks: A Review

The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

The Health Insurance Portability and Accountability Act.

In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Healthcare applications are considered as promising fields for wireless sensor networks, where patients can be monitored using wireless medical sensor networks (WMSNs). Current WMSN healthcare research trends focus on patient reliable communication, patient mobility, and energy-efficient routing, as a few examples. However, deploying new technologies in healthcare applications without considering security makes patient privacy vulnerable. Moreover, the physiological data of an individual are highly sensitive. Therefore, security is a paramount requirement of healthcare applications, especially in the case of patient privacy, if the patient has an embarrassing disease. This paper discusses the security and privacy issues in healthcare application using WMSNs. We highlight some popular healthcare projects using wireless medical sensor networks, and discuss their security. Our aim is to instigate discussion on these critical issues since the success of healthcare application depends directly on patient security and privacy, for ethic as well as legal reasons. In addition, we discuss the issues with existing security mechanisms, and sketch out the important security requirements for such applications. In addition, the paper reviews existing schemes that have been recently proposed to provide security solutions in wireless healthcare scenarios. Finally, the paper ends up with a summary of open security research issues that need to be explored for future healthcare applications using WMSNs.

https://www.mdpi.com/1424-8220/12/1/55/pdf

Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey

Wireless sensors have been integrated into a home sensing system and server/responder in a validated management scheme for home healthcare. The technology is well described and amply referenced. Heart rate, electrocardiogram, temperature, and accelerometer were employed at the front end, and integrated signals were transmitted from the patient station to the server. Feedback from the system could activate video surveillance, alerts to caregivers, and audio alerts to the patient. Power requirements were low and fidelity was high.

Elderly Patient Monitoring System Using a Wireless Sensor Network

Sensor grid applications in patient monitoring

Image compression is one of the advantageous techniques in different types of multi-media services. Image Compression technique have been emerged as one of the most important and successful applications in image analysis. In this paper the proposal of image compression using simple coding techniques called Huffman; Discrete Wavelet Transform (DWT) coding and fractal algorithm is done. These techniques are simple in implementation and utilize less memory. Huffman coding technique involves in reducing the redundant data in input images. DWT can be able to improve the quality of compressed image. Fractal algorithm involves encoding process and gives better compression ratio. By using the above algorithms the calculation of Peak signal to noise ratio(PSNR), Mean Square error(MSE) and compression ratio(CR) and Bits per pixel(BPP) of the compressed image by giving 512×512 input images and also the comparison of performance analysis of the parameters with that above algorithms is done. The result clearly explains that Fractal algorithm provides better Compression ratio (CR) and Peak Signal to noise ratio (PSNR).

Comparison of image compression techniques using huffman coding, DWT and fractal algorithm

The Brain-Computer Interface (BCI) is the technology that enables direct communication between the human brain and the external devices. Electroencephalography (EEG) proves to be the most studied measure of recording brain activity in BCI design. The paper is intended to analyze and extract the features of EEG signal and to classify the signal so that human emotions can be discriminated and serve as the control signal for BCI. The proposed method involves EEG data acquisition and processing which is done by feature extraction and classification of features at different frequency levels for Beta, Alpha, Theta and Delta waves. The Principal Component Analysis(PCA ), and the Wavelet Transform(WT) can be used for dimensionality reduction and feature extraction. The Artificial Neural Network (ANN) which is a computationally powerful model, is used as the classifier. The paper presents the comparison between the two approaches PCA and WT applied on the ANN Classifier.

EEG signal classification using Principal Component Analysis and Wavelet Transform with Neural Network

Water is a very inestimable resource and a driving force in irrigation. Optimal use of water is a need of the hour. Efficient irrigation watering helps in water conservation, getting better crop yields, reduce dependency on manures and improve crop quality. Various methods, both in test center and field including remote sensing, are accessible to measure soil moisture content, but the fastest and efficient one is with the use of soil moisture sensor electronic devices. For efficacious irrigation, it is obligatory to observe soil moisture content constantly in the irrigation fields. The soil moisture profile of the soil is an important aspect to make an irrigation decision. In this paper, an attempt is made to review how NodeMCU an ARM controller and soil moisture sensor pair up in easing the process of soil moisture profile with which the optimal usage of water for the different plant by only taking the soil moisture and weather parameters into considerations.

M. Pallikonda Rajasekaran

Papers

Elderly Patient Monitoring System Using a Wireless Sensor Network

Sensor grid applications in patient monitoring

Comparison of image compression techniques using huffman coding, DWT and fractal algorithm

EEG signal classification using Principal Component Analysis and Wavelet Transform with Neural Network

Implementation of Automatic Soil Moisture Dearth Test and Data Exertion Using Internet of Things