Fog computing based ultrasound nerve segmentation system using deep learning for mIoT

Internet of Things is an ever expanding field and applications can be used for medical field. Patient monitoring and diagnosis can be done with the help of IoT and the problems of storing large amount of data can be solved by using cloud computing. However, when transmitting large amount of data through the network, the latency will be impacted. This can be eliminated by introducing a fog layer for the processing of data and processed data later can be stored in the cloud. This study proposes a novel architecture for a hospital ultrasound system and deep learning algorithm is used for the nerve segmentation and a good accuracy is achieved

Effect of Finite Impulse Response Filters on Activities of Daily Living Classification Algorithms

With the increasing aging population, improving the healthcare system is an important task in every country. The largest number of hospitalizations of the elderly people is due to falls. Therefore, many researchers have come up with different fall detection mechanisms. Improving the accuracy of these algorithms is an important task. This paper focuses on the use of Finite Impulse Response filters to improve the accuracy

A Fog Computing-based System to Identify SARS - CoV -2 Using Deep Learning

SARS-CoV-2 has spread all over the world starting from 2019. One of the reasons for this widespread is because detection procedures were not in place. Another reason is that the detection methods like Polymerase Chain Reaction (PCR) test and antigen test are not fast enough and not accurate enough. Another method of detection of the disease is to use medical imaging. Ultrasounds have proven to have good accuracy over X ray scans and therefore, ultrasound images are used in this study to detect Covid 19 patients. For this purpose, a fog computing based deep learning technique is introduced which can be easily implemented in hospitals. This study achieved good accuracy

Deep learning based breast cancer detection system using fog computing

Among the different types of cancers, more women are suffering from breast cancer. Breast cancer can be identified by mammograms or using ultrasounds. Early detection of the cancer can be used to minimize the complexities the women will face. Deep learning based techniques such as convolutional neural networks (CNN) are used to detect the cancer from mammograms or ultrasound scans. In this study, VGGNet based CNN is used to detect the cancer cells. A novel architecture for collecting, processing and storing of patient data is proposed in this study involving a fog layer. This study achieved a high accuracy, sensitivity and specificity compared to previous studies

Fog Computing based Heart Disease Prediction System using Deep Learning for Medical IoT

Internet of Things (IoT) is used in all areas because of the benefits it is offering. All most anything can be connected to the internet and data created by these devices can be analyzed to predict results. IoT is helpful in the medical field because it can connect the patients with the healthcare professionals, and the healthcare professionals can monitor their patients remotely and analyze their data and take necessary actions. Because of the huge amount of data in IoT systems, cloud services are utilized to store the data. But this is not a feasible option in medical IoT, because the predictions should be available as quickly as possible, since patients’ lives are at risk. Therefore, edge-fog- cloud architecture is used. Fog nodes can be used to analyze data closer to the edge devices, resulting in much faster predictions and the cloud can be used for storage. This paper proposes a novel fog based architecture for medical IoT based on deep learning. Deep learning is used on the fog nodes to make accurate predictions. This study used data collected from heart patients to predict the heart disease to evaluate the system and yielded a good accuracy

A study of sustainable and safe signal processing techniques in wireless body sensor network for heart rate estimation with context awareness

Continuous monitoring of vital signs is helpful for the healthcare professionals in early diagnosis of diseases and takes preventive action. Blood pressure, heart rate are some of the vital signs that can be monitored using a wearable device. In order to help the healthcare professional in identifying the situation, context should be recorded. The objective of this research is to design a Body Sensor Network (BSN) to measure Heart Rate (HR) with context awareness sensing. In HR estimation, Motion Artifacts (MA), Least Mean Squares (LMS) algorithm is used. To collect the data, a device is manufactured which can transmit data wirelessly to a database. The selected signal processing methods are applied to these collected data to estimate HR along with the context of the user. Sustainable and Safe Signal Processing Techniques in Wireless Body Sensor Network for Heart Rate Estimation with Context Awareness is developed and performed well

IoT & Cloud Based Attendance Collection and Student Information Chatbot

The system proposed in this paper addresses two main concerns in the higher education system. One is efficient attendance monitoring of students, and the other concern is implementing an effective mechanism to interact with students to clarify their issues. As for the first concern, an IOT based attendance collection system will be proposed and for the second concern a cloud based chatbot is proposed. The main purpose of the proposed attendance collection system is to provide more efficient methodology to both students and lecturers while eliminating frequent issues such as buddy signing, loss of attendance sheets while improving student attendance scores. There are two parts to the attendance collection system. One is an IOT based device with biometric authentication for attendance collection which was developed using Arduino, microcontrollers, and fingerprint sensors. Second part is a web-based attendance system designed for lecturers or administrators for report generation, which was developed using Structured Query Language (SQL), PHP, JavaScript and HTML. The generated reports can be converted into excel or PDF files as required. As students are more familiar with smart devices than ever, a student information chatbot is proposed as a mechanism to interact with students to clarify their issues. The system establishes transparent and efficient communications with students for their general questions. The prototype chatbot was implemented in google cloud infrastructure and it is integrated with an android application which was built using android studio. The application can be used to interact with the chatbot and have the scalability for more features in the future