Novel Method of Improving Quality of Service for Voice over Internet Protocol Traffic in Mobile Ad Hoc Networks

In recent years, the application of Mobile Ad-hoc Network (MANET) with Voice over Internet Protocol (VoIP) has been increased.  However, the level of Quality of Service (QoS) for VoIP traffic in MANET, while there is no infrastructure, will reduce when dealing with a large number of calls. In this type of dynamic environment, the developing of a new infrastructure becomes more costly and time-consuming. In this paper, we proposed an efficient method, called the Quality of Service-Nearest Neighbor (QoS-NN), to improve the QoS level for VoIP in order to manage the huge number of calls over MANET network. We utilized the Ad-hoc On-demand Distance Vector (AODV) protocol as the underlying routing protocol to implement our proposed method. We evaluated the proposed QoS-NN method using Network Simulator version 2 (NS2). The performance of the proposed QoS-NN method was compared with Lexicographic order method. The comparison was evaluated in terms of R-factor, end-to-end delay, packet loss ratio, and packet delivery ratio performance metrics. In addition, the proposed method evaluated under different network parameters such as VoIP CODECs, node mobility speed, number of calls and number of nodes. The comparison results indicate that the proposed QoS-NN outperform the Lexicographic order method

Genome-Wide Association Study in a Lebanese Cohort Confirms PHACTR1 as a Major Determinant of Coronary Artery Stenosis

The manifestation of coronary artery disease (CAD) follows a well-choreographed series of events that includes damage of arterial endothelial cells and deposition of lipids in the sub-endothelial layers. Genome-wide association studies (GWAS) of multiple populations with distinctive genetic and lifestyle backgrounds are a crucial step in understanding global CAD pathophysiology. In this study, we report a GWAS on the genetic basis of arterial stenosis as measured by cardiac catheterization in a Lebanese population. The locus of the phosphatase and actin regulator 1 gene (PHACTR1) showed association with coronary stenosis in a discovery experiment with genome wide data in 1,949 individuals (rs9349379, OR = 1.37, p = 1.57×10−5). The association was replicated in an additional 2,547 individuals (OR = 1.31, p = 8.85×10−6), leading to genome-wide significant association in a combined analysis (OR = 1.34, p = 8.02×10−10). Results from this GWAS support a central role of PHACTR1 in CAD susceptibility irrespective of lifestyle and ethnic divergences. This association provides a plausible component for understanding molecular mechanisms involved in the formation of stenosis in cardiac vessels and a potential drug target against CAD

Large Scale Association Analysis Identifies Three Susceptibility Loci for Coronary Artery Disease

Genome wide association studies (GWAS) and their replications that have associated DNA variants with myocardial infarction (MI) and/or coronary artery disease (CAD) are predominantly based on populations of European or Eastern Asian descent. Replication of the most significantly associated polymorphisms in multiple populations with distinctive genetic backgrounds and lifestyles is crucial to the understanding of the pathophysiology of a multifactorial disease like CAD. We have used our Lebanese cohort to perform a replication study of nine previously identified CAD/MI susceptibility loci (LTA, CDKN2A-CDKN2B, CELSR2-PSRC1-SORT1, CXCL12, MTHFD1L, WDR12, PCSK9, SH2B3, and SLC22A3), and 88 genes in related phenotypes. The study was conducted on 2,002 patients with detailed demographic, clinical characteristics, and cardiac catheterization results. One marker, rs6922269, in MTHFD1L was significantly protective against MI (OR = 0.68, p = 0.0035), while the variant rs4977574 in CDKN2A-CDKN2B was significantly associated with MI (OR = 1.33, p = 0.0086). Associations were detected after adjustment for family history of CAD, gender, hypertension, hyperlipidemia, diabetes, and smoking. The parallel study of 88 previously published genes in related phenotypes encompassed 20,225 markers, three quarters of which with imputed genotypes The study was based on our genome-wide genotype data set, with imputation across the whole genome to HapMap II release 22 using HapMap CEU population as a reference. Analysis was conducted on both the genotyped and imputed variants in the 88 regions covering selected genes. This approach replicated HNRNPA3P1-CXCL12 association with CAD and identified new significant associations of CDKAL1, ST6GAL1, and PTPRD with CAD. Our study provides evidence for the importance of the multifactorial aspect of CAD/MI and describes genes predisposing to their etiology

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Measuring the extent of patient satisfacation with the services provided by health care in Madinah

The aim of this study was to identify the level of quality of healthcare services provided at the Madinah hospital and to measure the impact of the quality of healthcare services on patient satisfaction. The study also aimed to provide suggestions and recommendations to the hospital on how to improve the quality of healthcare services to positively impact patient satisfaction. The research will use a descriptive methodology, which involves identifying variables and phenomena related to the study, as well as an analytical methodology that analyzes the data obtained through research tools using statistical methods to achieve the study's purpose. The study found that there is a high level of quality of healthcare services in terms of their dimensions (tangibles, reliability, safety, empathy, responsiveness), as well as a high level of patient satisfaction with healthcare services in Madinah. Furthermore, there is a statistically significant positive correlation between the level of quality of healthcare services and patient satisfaction. Specifically, for every 1% increase in the quality of healthcare services, patient satisfaction increased by 0.546%.&nbsp

An IoT-Platform-Based Deep Learning System for Human Behavior Recognition in Smart City Monitoring Using the Berkeley MHAD Datasets

Internet of Things (IoT) technology has been rapidly developing and has been well utilized in the field of smart city monitoring. The IoT offers new opportunities for cities to use data remotely for the monitoring, smart management, and control of device mechanisms that enable the processing of large volumes of data in real time. The IoT supports the connection of instruments with intelligible features in smart cities. However, there are some challenges due to the ongoing development of these applications. Therefore, there is an urgent need for more research from academia and industry to obtain citizen satisfaction, and efficient architecture, protocols, security, and services are required to fulfill these needs. In this paper, the key aspects of an IoT infrastructure for smart cities were analyzed. We focused on citizen behavior recognition using convolution neural networks (CNNs). A new model was built on understanding human behavior by using the berkeley multimodal human action (MHAD) Datasets. A video surveillance system using CNNs was implemented. The proposed model&rsquo;s simulation results achieved 98% accuracy for the citizen behavior recognition system

Computational Linguistics Based Emotion Detection and Classification Model on Social Networking Data

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models

Planet Optimization with Deep Convolutional Neural Network for Lightweight Intrusion Detection in Resource-Constrained IoT Networks

Cyber security is becoming a challenging issue, because of the growth of the Internet of Things (IoT), in which an immense quantity of tiny smart gadgets push trillions of bytes of data over the Internet. Such gadgets have several security flaws, due to a lack of hardware security support and defense mechanisms, thus, making them prone to cyber-attacks. Moreover, IoT gateways present limited security features for identifying such threats, particularly the absence of intrusion detection techniques powered by deep learning (DL). Certainly, DL methods need higher computational power that exceeds the capability of such gateways. This article focuses on the development of Planet Optimization with a deep convolutional neural network for lightweight intrusion detection (PODCNN-LWID) in a resource-constrained IoT environment. The presented PODCNN-LWID technique primarily aims to identify and categorize intrusions. In the presented PODCNN-LWID model, two major processes are involved, namely, classification and parameter tuning. At the primary stage, the PODCNN-LWID technique applies a DCNN model for the intrusion identification process. Next, in the second stage, the PODCNN-LWID model utilizes the PO algorithm as a hyperparameter tuning process. The experimental validation of the PODCNN-LWID model is carried out on a benchmark dataset, and the results are assessed using varying measures. The comparison study reports the enhancements of the PODCNN-LWID model over other approaches

Planet Optimization with Deep Convolutional Neural Network for Lightweight Intrusion Detection in Resource-Constrained IoT Networks

Cyber security is becoming a challenging issue, because of the growth of the Internet of Things (IoT), in which an immense quantity of tiny smart gadgets push trillions of bytes of data over the Internet. Such gadgets have several security flaws, due to a lack of hardware security support and defense mechanisms, thus, making them prone to cyber-attacks. Moreover, IoT gateways present limited security features for identifying such threats, particularly the absence of intrusion detection techniques powered by deep learning (DL). Certainly, DL methods need higher computational power that exceeds the capability of such gateways. This article focuses on the development of Planet Optimization with a deep convolutional neural network for lightweight intrusion detection (PODCNN-LWID) in a resource-constrained IoT environment. The presented PODCNN-LWID technique primarily aims to identify and categorize intrusions. In the presented PODCNN-LWID model, two major processes are involved, namely, classification and parameter tuning. At the primary stage, the PODCNN-LWID technique applies a DCNN model for the intrusion identification process. Next, in the second stage, the PODCNN-LWID model utilizes the PO algorithm as a hyperparameter tuning process. The experimental validation of the PODCNN-LWID model is carried out on a benchmark dataset, and the results are assessed using varying measures. The comparison study reports the enhancements of the PODCNN-LWID model over other approaches

Queuing theory approach for NDN mobile producer’s rate of transmission using network coding

Nowadays, Internet has become a crucial part of everyday life in areas of academic research, healthcare, military, commercial industries, transportation, and entertainment. The Internetwork was not invented for its present-day state of operation. Numerous limitations are currently emerging in terms of Internet performance, mobility, scalability, dependability, security, and service quality. In this paper we consider real time network application for voice and video, both having variable bitrate sources. A mobile producer is solely responsible to direct the traffic to a specified destination, either in one network or different. For voice traffic, a constant bitrate (CBR) is generated in real time during a talkspurt. During this generation interval, a small amount of traffic is transmitted in a period of silent pause. For video traffic, the bitrate is changed because video coding form is altering due to time interval. Also, the visible image in the proposed video application varies due to network coding application. In the results, performance analysis is measured due to amount of time spend in each cell residence and signalling cost between mobile producer and rendezvous node as a definite destination. We conducted simulation coding in ndnSIM 2.1 and performance analysis using Python. Simulation output present better results compared with other benchmarked research