Security-Aware Routing on Wireless Communication for E-Health Records Monitoring Using Machine Learning

An ad hoc structure is self-organizing, self-forming, and system-free, with no nearby associations. One of the significant limits we must focus on in frameworks is leading. As for directions, we can send the packet or communications from the sender to the recipient node. AODV Routing Protocol, a short display that will make the tutorial available on demand. Machine Learning (ML) based IDS must be integrated and perfected to support the detection of vulnerabilities and enable frameworks to make intrusion decisions while ML is about their mobile context. This paper considers the combined effect of stooped difficulties along the way, problems at the medium get-right-of-area to impact layer, or pack disasters triggered by the remote control going off route. The AODV as the Routing MANET protocol is used in this work, and the process is designed and evaluated using Support Vector Machine (SVM) to detect the malicious network nodes.

The Novel Multi-layered Approach to Enhance Sorting Performance of Healthcare Analysis

Emergence of big data in today’s world leads to new challenges for sorting strategies to analyze the data in a better way. For most of the analyzing technique, sorting is considered as an implicit attribute of the technique used. The availability of huge data has changed the way data is analyzed across industries. Healthcare is one of the notable areas where data analytics is making big changes. An efficient analysis has the potential to reduce costs of treatment and improve the quality of life in general. Healthcare industries are collecting massive amounts of data and look for the best strategies to use these numbers. This research proposes a novel non-comparison based approach to sort a large data that can further be utilized by any big data analytical technique for various analyses.

The Psychological Impact of the COVID-19 Pandemic on Jordanian Healthcare Workers

Introduction: Healthcare workers face incomparable work and psychological demands that are amplified throughout the COVID-19 pandemic. Aim: This study aimed to investigate the psychological impact of the COVID-19 pandemic on health care workers in Jordan. Method: A cross-sectional design was used. Data was collected using an online survey during the outbreak of COVID-19. Results: Overall, of the 312 healthcare workers, almost 38% and 36% presented with moderate to severe anxiety and depression consecutively. Nurses reported more severe symptoms than other healthcare workers. And both anxiety and depression were negatively correlated with well-being. Getting infected was not an immediate worry among healthcare workers; however, they were worried about carrying the virus to their families. Implications for Practice: Stakeholders must understand the impact of COVID-19 on healthcare workers and plan to provide them with the required psychological support and interventions at an early stage.

Smart Healthcare Security Device on Medical IoT Using Raspberry Pi

This paper aims to improve the protection of two-wheelers. This study is divided into two parts: a helmet unit and a vehicle unit. The primary unit is the helmet unit, which contains a sensor, and the second part is known as the alcohol sensor, which is used to determine whether or not the driver is wearing the user helmet correctly. This data is then transmitted to the vehicle unit via the RF transmitter. The data is encoded with the aid of an encoder. Suppose the alcohol sensor senses that the driver is intoxicated. In that case, the IoT-based Raspberry Pi micro-controller passes the data to the vehicle unit via the RF transmitter, which immediately stops the vehicle from using the Driver circuit to control the relay. To stop the consumption of alcohol, the vehicles would be tracked daily. If the individual driving the vehicle is under the influence of alcohol while driving, the buzzer will automatically trigger. The vehicle key will be switched off.

Secured and Privacy-Based IDS for Healthcare Systems on E-Medical Data Using Machine Learning Approach

Existing methods use static path identifiers, making it easy for attackers to conduct DDoS flooding attacks. Create a system using Dynamic Secure aware Routing by Machine Learning (DAR-ML) to solve healthcare data. A DoS detection system by ML algorithm is proposed in this paper. First, to access the user to see the authorized process. Next, after the user registration, users can compare path information through correlation factors between nodes. Then, choose the device that will automatically activate and decrypt the data key. The DAR-ML is traced back to all healthcare data in the end module. In the next module, the users and admin can describe the results. These are the outcomes of using the network to make it easy. Through a time interval of 21.19% of data traffic, the findings demonstrate an attack detection accuracy of over 98.19%, with high precision and a probability of false alarm.

Review on Reliable and Quality Wearable Healthcare Device (WHD)

This paper presents a brief review on present developments in wearable devices and their importance in healthcare networks. The state-of-the-art system architecture on wearable healthcare devices and their design techniques are reviewed and becomes an essential step towards developing a smart device for various biomedical applications which includes diseases classifications and detection, analyzing nature of the bio signals, vital parameters measurement, and e-health monitoring through noninvasive method. From the review on latest published research papers on medical wearable device and bio signal analysis, it can be concluded that it is more important and very essential to design and develop a smart wearable device in healthcare environment for quality signal acquisition and e-health monitoring which leads to effective measures of multiparameter extractions. This will help the medical practitioners to understand the nature of patient health condition easily by visualizing a quality signal by smart wearable devices.

AI-Enabled Support System for Melanoma Detection and Classification

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

Analyzing Healthcare Data Using Water Wave Optimization-Based Clustering Technique

The medical informatics field gets wide attention among the research community while developing a disease diagnosis expert system for useful and accurate predictions. However, accuracy is one of the major medical informatics concerns, especially for disease diagnosis. Many researchers focused on the disease diagnosis system through computational intelligence methods. Hence, this paper describes a new diagnostic model for analyzing healthcare data. The proposed diagnostic model consists of preprocessing, diagnosis, and performance evaluation phases. This model implements the water wave optimization (WWO) algorithm to analyze the healthcare data. Before integrating the WWO algorithm in the proposed model, two modifications are inculcated in WWO to make it more robust and efficient. These modifications are described as global information component and mutation operator. Several performance indicators are applied to assess the diagnostic model. The proposed model achieves better results than existing models and algorithms.

Using Patch ECG Device During Lower Third Molar Surgery

This study aimed to evaluate cardiac activity changes during lower third molar surgery concerning gender and anxiety levels. Thirty healthy subjects who required lower third molar surgery filled out Norman Corah dental anxiety scale (DAS) before surgery. A patch ECG device (Savvy, Institute ''Jožef Stefan'', Ljubljana, Slovenia) was applied to the patient to evaluate heart rate (HR) and heart rhythm. These parameters were assessed in 8 different intervals. Periods of the highest mean HR values—incision and flap elevation compared to the period with minimal mean HR values—during suturing showed statistical significance difference (p<0.05). The most common outstanding ECG finding was sinus tachycardia, especially in anxious compared to non-anxious patients. Extraction difficulty score was correlated with the procedure duration time and with the abnormal ECG findings in the period of tooth extraction. Significant cardiac activity changes are detected during surgery. Physiological manifestations of anxiety may be evaluated successfully using a patch ECG device.

Age and Education as Major Determinants of HIS's Adoption

This study explored the perceptions of 264 nurses regarding their satisfaction with health information systems (HIS) in one of the public hospitals in the GCC area. The study adopted the information system success model and tried to predict satisfaction level utilizing information quality, system quality, and service quality. Results supported the role of information quality and service quality in predicting satisfaction. In addition, four factors were used as moderators of relationships assumed. Three moderation effects were witnessed: gender moderated the relationship between service quality and satisfaction, age moderated the relationship between service quality and satisfaction, and also moderated the relationship between system quality and satisfaction. A set of one-way ANOVA tests were used to compare different perceptions based on the four demographic factors (gender, age, education, and experience) on the item and construct levels. Conclusions and a summary of all results are reported in this study.