Performance Ameliorations of AODV by Black Hole Attack Detection Utilizing IDSAODV as Well as Reverse AODV

The so-called Black Hole Attack is among the most perilous and widespread security attacks in MANET nets, researchers have been tasked with developing strategies to detect it. Two of these methods are the Intrusion Detection System AODV (IDSAODV) as well as the Extended AODV. The present paper attempts to investigate the impact of a Black Hole Attack on the functionality of the network in the existence of single or more attackers. It also evaluates the Extended AODV and IDSAODV in a net in order to see how effectively they could detect and mitigate the attack. For the aim of evaluating its performance, the researchers utilized Throughput, Normalized Routing Load (NRL), and Packet Delivery Ratio (PDR). The comprehensive simulation results show that the IDSAODV application decreased the effect of the attacks. However, it raised the rate of packet delivery to sixty eight percent at the identical time. Reverse AODV, on the other hand, provided superior outcomes, with a PDR of 100%, but also resulted in an exceedingly higher NRL than the IDSAODV. Likewise, the simulation findings demonstrated that the attacking node's position tormented the IDSAODV's functionality

Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks

Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy

Zero Algorithms for Avoiding Crosstalk in Optical Multistage Interconnection Network

Multistage Interconnection Networks (MINs) are popular in switching and communication applications. It had been used in telecommunication and parallel computing systems for many years. The broadband switching networks are built from 2 x 2 electro-optical switches such as Lithium Niobate switches. Each switch has two active inputs and outputs. Optical signals, carried on either inputs are coupled to either outputs by applying an appropriate voltage to the switch. One of the problems associated with these electro-optical switches is the crosstalk problem, which is caused by undesired coupling between signals carried in two waveguides. This thesis propose an efficient solution to avoid crosstalk, which is routing of traffic through an N x N optical network to avoid coupling two signals within each switching element. Under the constraint of avoiding crosstalk, the research interest is to realize a permutation that will use the minimum number of passes (to route the input request to output without crosstalk). This routing problem is an NP-hard problem. Many heuristic algorithms have been proposed and designed to perform the routing such as the sequential algorithm, the sequential down algorithm, the degree-ascending algorithm, the degree-descending algorithm, the Simulated Annealing algorithm and the Ant Colony algorithm. The Zero algorithms are the new algorithms that have been proposed in this thesis. In Zero algorithms, there are three types of algorithms namely; The Zero X, Zero Y and zeroXY algorithms. The experiments conducted have proven that the proposed algorithms are effective and efficient. They are based on routing algorithms to minimize the number of passes to route all the inputs to outputs without crosstalk. In addition, these algorithms when implemented with partial ZeroX and ZeroY algorithms would yield the same results as the other heuristic algorithms, but over performing them when the execution time is considered. Zero algorithms have been tested with many cases and the results are compared to the results of the other established algorithms. The performance analysis showed the advantages of the Zero algorithms over the other algorithms in terms of average number of passes and execution time

Using deep learning to detecting abnormal behavior in internet of things

The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy

A new algorithm for routing and scheduling in optical omega network

Multistage interconnection networks (MIN) are popular in switching and communication applications. However, OMINs introduce crosstalk which results from coupling two signals within one Switching Element (SE). Under the constraint of avoiding crosstalk, what we will discuss in is how to realize a permutation that requires the minimum number of passes. In this paper, we are interested in a network called Omega Network, which has shuffle-exchange connection pattern. We propose a new algorithm called the ZeroY algorithm (ZeroY) to avoid crosstalk and route the traffic in an OM IN more efficiently. The results of the ZeroY algorithm are analyzed and compared with those of other algorithms (except the GA) in an Omega network. The ZeroY algorithm outperforms all the other algorithms in terms of the running time that are required for one permutation

Analyzing the Effectiveness of Hand Washing Programs in Reducing Hospital Infection Rates

A total of 27 articles were included in this review, and data were extracted and reviewed using a data matrix. A qualitative synthesis was employed. Findings showed that hand washing can be effective in reducing hospital-acquired infections, but the behavior is complex and multifaceted, and there is no one universally effective intervention. Several specific hand hygiene interventions designed to improve compliance that were related to decreases in infection rates were identified. These components for success included effective educational strategies, access to continual resources, integrating the hand hygiene program into an organizational culture, and dissemination of results. (Mouajou et al., 2022) The aim of the study was to analyze the effectiveness of hand washing programs in reducing hospital-acquired infections and identify the components of a successful hand hygiene behavior intervention. An integrative review was employed in this study and articles were retrieved from three databases: PubMed, CINAHL, and Academic Search Premier. Searches were initially conducted using key terms such as hand washing, hand hygiene, and nosocomial infections. Articles were included if the populations were hospital patients and healthcare workers, the interventions involved hand hygiene behavior, and the outcomes were changes in infection rates or compliance with the intervention. Primary and secondary articles were selected using additional search criteria, and articles were critically appraised and classified according to the level of evidence

Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach

The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergence related issues is proposed. Additionally, to reduce the impact of translation errors and handle instance selection problem, we propose a clustering-based bee-colony-sample selection method for the optimal selection of most distinguishing features representing the target data. To evaluate the proposed model, various experiments are conducted employing an English-Arabic cross-lingual data set. Simulations results demonstrate that the proposed model outperforms the baseline approaches in terms of classification performances. Furthermore, the statistical outcomes indicate the advantages of the proposed training data sampling and target-based feature selection to reduce the negative effect of translation errors. These results highlight the fact that the proposed approach achieves a performance that is close to in-language supervised models

Oral manifestations in young adults infected with COVID-19 and impact of smoking:a multi-country cross-sectional study

Background: Oral manifestations and lesions could adversely impact the quality of people's lives. COVID-19 infection may interact with smoking and the impact on oral manifestations is yet to be discovered. Objectives: The aim of this study was to assess the self-reported presence of oral lesions by COVID-19-infected young adults and the differences in the association between oral lesions and COVID-19 infection in smokers and non-smokers. Methods: This cross-sectional multi-country study recruited 18-to-23-year-old adults. A validated questionnaire was used to collect data on COVID-19-infection status, smoking and the presence of oral lesions (dry mouth, change in taste, and others) using an online platform. Multi-level logistic regression was used to assess the associations between the oral lesions and COVID-19 infection; the modifying effect of smoking on the associations. Results: Data was available from 5,342 respondents from 43 countries. Of these, 8.1% reported COVID-19-infection, 42.7% had oral manifestations and 12.3% were smokers. A significantly greater percentage of participants with COVID-19-infection reported dry mouth and change in taste than non-infected participants. Dry mouth (AOR=, 9=xxx) and changed taste (AOR=, 9=xxx) were associated with COVID-19-infection. The association between COVID-19-infection and dry mouth was stronger among smokers than non-smokers (AOR = 1.26 and 1.03, p = 0.09) while the association with change in taste was stronger among non-smokers (AOR = 1.22 and 1.13, p = 0.86). Conclusion: Dry mouth and changed taste may be used as an indicator for COVID-19 infection in low COVID-19-testing environments. Smoking may modify the association between some oral lesions and COVID-19-infection

Cigarettes' use and capabilities-opportunities-motivation-for-behavior model:a multi-country survey of adolescents and young adults

The use of cigarettes among adolescents and young adults (AYA) is an important issue. This study assessed the association between regular and electronic-cigarettes use among AYA and factors of the Capability-Motivation-Opportunity-for-Behavior-change (COM-B) model. A multi-country survey was conducted between August-2020 and January-2021, Data was collected using the Global-Youth-Tobacco-Survey and Generalized-Anxiety-Disorder-7-item-scale. Multi-level logistic-regression-models were used. Use of regular and electronic-cigarettes were dependent variables. The explanatory variables were capability-factors (COVID-19 status, general anxiety), motivation-factors (attitude score) and opportunity-factors (country-level affordability scores, tobacco promotion-bans, and smoke free-zones) controlling for age and sex. Responses of 6,989-participants from 25-countries were used. Those who reported that they were infected with COVID-19 had significantly higher odds of electronic-cigarettes use (AOR = 1.81, P = 0.02). Normal or mild levels of general anxiety and negative attitudes toward smoking were associated with significantly lower odds of using regular-cigarettes (AOR = 0.34, 0.52, and 0.75, P < 0.001) and electronic-cigarettes (AOR = 0.28, 0.45, and 0.78, P < 0.001). Higher affordability-score was associated with lower odds of using electronic-cigarettes (AOR = 0.90, P = 0.004). Country-level-smoking-control policies and regulations need to focus on reducing cigarette affordability. Capability, motivation and opportunity factors of the COM-B model were associated with using regular or electronic cigarettes

Anxiety among adolescents and young adults during COVID-19 pandemic: A multi-country survey

(1) Background: Adolescents-and-young-adults (AYA) are prone to anxiety. This study assessed AYA's level of anxiety during the COVID-19 pandemic; and determined if anxiety levels were associated with country-income and region, socio-demographic profile and medical history of individuals. (2) Methods: A survey collected data from participants in 25 countries. Dependent-variables included general-anxiety level, and independent-variables included medical problems, COVID-19 infection, age, sex, education, and country-income-level and region. A multilevel-multinomial-logistic regression analysis was conducted to determine the association between dependent, and independent-variables. (3) Results: Of the 6989 respondents, 2964 (42.4%) had normal-anxiety, and 2621 (37.5%), 900 (12.9%) and 504 (7.2%) had mild, moderate and severe-anxiety, respectively. Participants from the African region (AFR) had lower odds of mild, moderate and severe than normal-anxiety compared to those from the Eastern-Mediterranean-region (EMR). Also, participants from lower-middle-income-countries (LMICs) had higher odds of mild and moderate than normal-anxiety compared to those from low-income-countries (LICs). Females, older-adolescents, with medical-problems, suspected-but-not-tested-for-COVID-19, and those with friends/family-infected with COVID-19 had significantly greater odds of different anxiety-levels. (4) Conclusions: One-in-five AYA had moderate to severe-anxiety during the COVID-19-pandemic. There were differences in anxiety-levels among AYAs by region and income-level, emphasizing the need for targeted public health interventions based on nationally-identified priorities