QoS Parametric Inspection of Uniform and Assorted Trajectories for MANET Routing Protocols

A Mobile Ad Hoc Network (MANET) is a self-governing network of mobile nodes without the inclusion of any wired links. Each node can move in an ad hoc manner and therefore, such a network should consist of routing protocols which can adapt to dynamically changing topologies. Numerous protocols have been proposed for the same. However, the trajectories followed by the individual nodes have not been distinctly dealt with. This paper presents a meticulous study on QoS parameters of proactive (OLSR) and reactive (DSR) protocols of MANETs for uniform as well as dissimilar trajectories of individual nodes in a small network of about 20 nodes. Also an examination of partial node failures for both the above mentioned protocols has been done. The performance metrics utilized in this study are average throughput and average delay. OPNET modeler has been utilized for this study. This assessment shows that for uniform trajectories, OLSR has almost same average delay but a higher average throughput as compared to DSR. Also it is seen that, as compared to uniform trajectories, non-uniform trajectories deliver a much higher average throughput. Node failures only reduce average throughputs whereas average delays remain unchanged.

A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

Artificial Intelligence(AI)is finding increasing application in healthcare monitoring.Machine learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health data.Thus,early detection of any disease or derangement can aid doctors in saving patients’lives.However,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper classification.We propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health status.To begin with,various patient datasets were collected and trained into the system using IoT sensors.As a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result visibility.Additionally,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant information.Then,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a prescription.We found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

Attention-Based Deep Learning Model for Early Detection of Parkinson’s Disease

Parkinson’s disease(PD),classified under the category of a neurological syndrome,affects the brain of a person which leads to the motor and non-motor symptoms.Among motor symptoms,one of the major disabling symptom is Freezing of Gait(FoG)that affects the daily standard of living of PD patients.Available treatments target to improve the symptoms of PD.Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual.This work proposed a novel attention-basedmodel for the detection of FoG events and PD,andmeasuring the intensity of PD on the United Parkinson’s Disease Rating Scale.Two separate datasets,that is,UCF Daphnet dataset for detection of Freezing of Gait Events and PhysioNet Gait in PD Dataset were used for training and validating on their respective problems.The results show a definite rise in the various performance metrics when compared to landmark models on these problems using these datasets.These results strongly suggest that the proposed state of the art attention-based deep learning model provide a consistent as well as an efficient solution to the selected problem.High valueswere obtained for various performance metrics like accuracy of 98.74%for detection FoG,98.72%for detection of PD and 98.05%for measuring the intensity of PD on UPDRS.The model was also analyzed for robustness against noisy samples,where also model exhibited consistent performance.These results strongly suggest that the proposed model provides a better classification method for selected problem.

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and computation.The services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and content-sharing.These issues lead to computation and communication overhead of servers and computation nodes.To address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and computation.First,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead.Second,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge devices.The simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

Estimating Vertex Measures in Social Networks by Sampling Completions of RDS Trees

This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of “estimating connectivity from spanning tree completions” (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of trying to obtain final edge or vertex rosters, and instead aims to estimate network-centric properties of vertices probabilistically from the spanning trees themselves. In this paper, we discuss the problem of missing data and describe the protocols of our completion method, and finally the results of an experiment where ECSTC was used to estimate graph dependent vertex properties from spanning trees sampled from a graph whose characteristics were known ahead of time. The results show that ECSTC methods hold more promise for obtaining network-centric properties of individuals from a limited set of data than researchers may have previously assumed. Such an approach represents a break with past strategies of working with missing data which have mainly sought means to complete the graph, rather than ECSTC’s approach, which is to estimate network properties themselves without deciding on the final edge set.

Dynamic Optimization of Caregiver Schedules Based on Vital Sign Streams

Hospital facilities use a collection of heterogeneous devices, produced by many different vendors, to monitor the state of patient vital signs. The limited interoperability of current devices makes it difficult to synthesize multivariate monitoring data into a unified array of real-time information regarding the patients state. Without an infrastructure for the integrated evaluation, display, and storage of vital sign data, one cannot adequately ensure that the assignment of caregivers to patients reflects the relative urgency of patient needs. This is an especially serious issue in critical care units (CCUs). We present a formal mathematical model of an operational critical care unit, together with metrics for evaluating the systematic impact of caregiver scheduling decisions on patient care. The model is rich enough to capture the essential features of device and patient diversity, and so enables us to test the hypothesis that integration of vital sign data could realistically yield a significant positive impact on the efficacy of critical care delivery outcome. To test the hypothesis, we employ the model within a computer simulation. The simulation enables us to compare the current scheduling processes in widespread use within CCUs, against a new scheduling algorithm that makes use of an integrated array of patient information collected by an (anticipated) vital sign data integration infrastructure. The simulation study provides clear evidence that such an infrastructure reduces risk to patients and lowers operational costs, and in so doing reveals the inherent costs of medical device non-interoperability.

Quantifying Malware Evolution through Archaeology

Dynamic analysis of malware allows us to examine malware samples, and then group those samples into families based on observed behavior. Using Boolean variables to represent the presence or absence of a range of malware behavior, we create a bitstring that represents each malware behaviorally, and then group samples into the same class if they exhibit the same behavior. Combining class definitions with malware discovery dates, we can construct a timeline of showing the emergence date of each class, in order to examine prevalence, complexity, and longevity of each class. We find that certain behavior classes are more prevalent than others, following a frequency power law. Some classes have had lower longevity, indicating that their attack profile is no longer manifested by new variants of malware, while others of greater longevity, continue to affect new computer systems. We verify for the first time commonly held intuitions on malware evolution, showing quantitatively from the archaeological record that over 80% of the time, classes of higher malware complexity emerged later than classes of lower complexity. In addition to providing historical perspective on malware evolution, the methods described in this paper may aid malware detection through classification, leading to new proactive methods to identify malicious software.

Auditory filter based broadband MUSIC algorithm for sound source localization

Based on the analysis of the shortcomings of broadband MUSIC algorithm with short-time Fourier transform(SF-MUSIC) for sound source localization,a broadband MUSIC algorithm with auditory filter(AF-MUSIC) was proposed.The proposed algorithm first employs auditory filter bank to decompose the signals received on the microphone array,and then locates the sound source with MUSIC algorithm over every frequency channel.At last,by combining with the subinterval frequency estimation,the final localization result is gained.Evaluations on the proposed algorithm prove that comparing with the SF-MUSIC algorithm,the AF-MUSIC algorithm decreases the average error of the estimation results with 2.5479 degree in different source conditions.The accuracy of sound source DOA estimation is enhanced effectively.

Correlating Bladder Cancer Risk Genes with Their Targeting MicroRNAs Using MMiRNA-Tar

The Cancer Genome Atlas(TCGA)(http://cancergenome.nih.gov) is a valuable data resource focused on an increasing number of well-characterized cancer genomes. In part, TCGA provides detailed information about cancer-dependent gene expression changes, including changes in the expression of transcription-regulating micro RNAs. We developed a web interface tool MMi RNA-Tar(http://bioinf1.indstate.edu/MMi RNA-Tar) that can calculate and plot the correlation of expression for m RNA micro RNA pairs across samples or over a time course for a list of pairs under different prediction confidence cutoff criteria. Prediction confidence was established by requiring that the proposed m RNA micro RNA pair appears in at least one of three target prediction databases: Target Profiler, Target Scan, or mi Randa. We have tested our MMi RNA-Tar tool through analyzing 53 tumor and 11 normal samples of bladder urothelial carcinoma(BLCA)datasets obtained from TCGA and identified 204 micro RNAs. These micro RNAs were correlated with the m RNAs of five previously-reported bladder cancer risk genes and these selected pairs exhibited correlations in opposite direction between the tumor and normal samples based on the customized cutoff criterion of prediction. Furthermore, we have identified additional 496 genes(830pairs) potentially targeted by 79 significant micro RNAs out of 204 using three cutoff criteria, i.e.,false discovery rate(FDR) < 0.1, opposite correlation coefficient between the tumor and normal samples, and predicted by at least one of three target prediction databases. Therefore, MMi RNATar provides researchers a convenient tool to visualize the co-relationship between micro RNAs and m RNAs and to predict their targeting relationship. We believe that correlating expression profiles for micro RNAs and m RNAs offers a complementary approach for elucidating their interactions.