A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust.These systems are used to improve physicians’diagnostic processes in terms of speed and accuracy.Using data-mining techniques,a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms.In this work,we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’information and exposing them to cyber and network attacks.Solving the same problem with a different methodology,the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms.Moreover,our model is validated against the privacy requirements of the hospitals’datasets and votes,and patients’diagnosed symptoms.

Privacy-Preserving Healthcare System for Clinical Decision-Support and Emergency Call Systems

Healthcare centers always aim to deliver the best quality healthcare services to patients and earn their satisfaction. Technology has played a major role in achieving these goals, such as clinical decision-support systems and mobile health social networks. These systems have improved the quality of care services by speeding-up the diagnosis process with accuracy, and allowing caregivers to monitor patients remotely through the use of WBS, respectively. However, these systems’ accuracy and efficiency are dependent on patients’ health information, which must be inevitably shared over the network, thus exposing them to cyber-attacks. Therefore, privacy-preserving services are ought to be employed to protect patients’ privacy. In this work, we proposed a privacy-preserving healthcare system, which is composed of two subsystems. The first is a privacy-preserving clinical decision-support system. The second subsystem is a privacy-preserving Mobile Health Social Network (MHSN). The former was based on decision tree classifier that is used to diagnose patients with new symptoms without disclosing patients’ records. Whereas the latter would allow physicians to monitor patients’ current condition remotely through WBS;thus sending help immediately in case of a distress situation detected. The social network, which connects patients of similar symptoms together, would also provide the service of seeking help of near-by passing people while the patient is waiting for an ambulance to arrive. Our model is expected to improve healthcare services while protecting patients’ privacy.

Performance Improvement of Multi-User Multiple-Input Multiple-Output Protocol for WLAN

The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.

On the Performance of Traffic Locality Oriented Route Discovery Algorithm with Delay

In MANETs, traffic may follow certain pattern that is not necessarily spatial or temporal but rather to follow special needs as a part of group for collaboration purposes. The source node tends to communicate with a certain set of nodes more than others regardless of their location exhibiting traffic locality where this set changes over time. We introduce a traffic locality oriented route discovery algorithm with delay, TLRDA-D. It utilises traffic locality by establishing a neighbourhood that includes the most likely destinations for a particular source node. The source node broadcasts the route request according to the original routing used. However, each intermediate node broadcasts the route request with a delay beyond this boundary to give priority for route requests that are travelling within their own source node’s neighbourhood region. This ap-proach improves the end-to-end delay and packet loss, as it generates less contention throughout the network. TLRDA-D is analysed using simulation to study the effect of adding a delay to route request propagation and to decide on the amount of the added delay.

FMAC: Fair Mac Protocol for Achieving Proportional Fairness in Multi-Rate WSNs

In a multi-rate wireless environment, slow nodes occupy the channel for longer time than fast nodes and thus the total throughput of the network will be reduced. In this research, we study the problem of fairness in multi-rate wireless sensor networks. To improve the fairness, we propose a new protocol, FMAC (Fair MAC protocol) that is based on IEEE 802.11 MAC protocol to achieve proportional fairness between all nodes. FMAC protocol includes medium delay periods within Backoff algorithm to utilize the idle slots of time and reduce the number of collisions and then number of retransmissions, and thus reducing the energy consumption, which is very critical in wireless sensor networks. The experimental results show that transmissions become faster with less collisions and power consumption when applying FMAC, while the aggregated throughput and proportional fairness are increased. The detailed performance evaluation and comparisons are provided using the simulation.

Enhancing Performance of Multi-Rate WLANs: Ant Colony Approach

The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified coefficient of variation (SCV) to closely reflect our topic. Through controlling the power of Access Points, SCV can optimize and improve the performance. Since our topic is a NP-hard problem, we use Ant Colony Algorithm to solve our model in a practical scenario. The simulation shows excellent results indicating that our model is efficient and superior to an existing method. Also we use software SAS to further reveal the relationships among the three indicators to illustrate the essence of our approach and an existing algorithm.