An efficient algorithm for monitoring virtual machines in clouds

Cloud computing systems consist of a pool of Virtual Machines (VMs), which are installed physically on the provider's set up. The main aim of the VMs is to offer the service to the end users. With the current increasing demand for the cloud VMs, there is always a huge requirement to secure the cloud systems. To keep these cloud systems secured, they need a continuous and a proper monitoring. For the purpose of monitoring, several algorithms are available with FVMs. FVM is a forensic virtual machine which monitors the threats among the VMs. Our formulated algorithm runs on FVM. In this paper, we formulate the Random-Start-Round-Robin algorithm for monitoring inside FVM

Studying Users Interactions and Behavior In Social Media Using Natural Language Processing

Social media platforms have been growing at a rapid pace, attracting users\u27 engagement with the online content due to their convenience facilitated by many useful features. Such platforms provide users with interactive options such as likes, dislikes as well as a way of expressing their opinions in the form of text (i.e., comments). As more people engage in different social media platforms, such platforms will increase in both size and importance. This growth in social media data is becoming a vital new area for scholars and researchers to explore this new form of communication. The huge data from social media has been a massive aid to researchers in the mission of exploring the public\u27s behavior and opinion pursuing different venues in social media research. In recent years, social media platforms have facilitated the way people communicate and interact with each other. The recent approach in analyzing the human language in social media has been mostly powered by the use of Natural Language Processing (NLP) and deep learning techniques. NLP techniques are some of the most promising methods used in social media analyses, including content categorization, topic discovery and modeling, sentiment analysis. Such powerful methods have boosted the process of understanding human language by enabling researchers to aggregate data relating to certain events addressing several social issues. The ability of posting comments on these online platforms has allowed some users to post racist and obscene contents, and to spread hate on these platforms. In some cases, this kind of toxic behavior might turn the comment section from a space where users can share their views to a place where hate and profanity are spread. Such issues are observed across various social media platforms and many users are often exposed to these kinds of behaviors which requires comment moderators to spend a lot of time filtering out these inappropriate comments. Moreover, such textual inappropriate contents can be targeted towards users irrespective of age, concerning a variety of topics not only controversial, and triggered by various events. Our work is primarily focused on studying, detecting and analyzing users\u27 exposure to this kind of toxicity on different social media platforms utilizing state-of-art techniques in both deep learning and natural language processing areas, and facilitated by exclusively collected and curated datasets that address various domains. The different domains, or applications, benefit from a unified and versatile pipeline that could be applied to various scenarios. Those applications we target in this dissertation are: (1) the detection and measurement of kids\u27 exposure to inappropriate comments posted on YouTube videos targeting young users, (2) the association between topics of contents cover by mainstream news media and the toxicity of the comments and interactions by users, (3) the user interaction with, sentiment, and general behavior towards different topics discussed in social media platforms in light of major events (i.e., the outbreak of the COVID-19 pandemic). Our technical contribution is not limited to only the integration of the various techniques borrowed from the deep learning and natural language processing literature to those new and emerging problem spaces, for socially relevant computing problems, but also in comprehensively studying various approaches to determine their feasibility and relevant to the discussed problems, coupled with insights on the integration, as well as a rich set of conclusions supported with systematic measurements and in-depth analyses towards making the online space safer

High availability of data using Automatic Selection Algorithm (ASA) in distributed stream processing systems

High Availability of data is one of the most critical requirements of a distributed stream processing systems (DSPS). We can achieve high availability using available recovering techniques, which include (active backup, passive backup and upstream backup). Each recovery technique has its own advantages and disadvantages. They are used for different type of failures based on the type and the nature of the failures. This paper presents an Automatic Selection Algorithm (ASA) which will help in selecting the best recovery techniques based on the type of failures. We intend to use together all different recovery approaches available (i.e., active standby, passive standby, and upstream standby) at nodes in a distributed stream-processing system (DSPS) based upon the system requirements and a failure type). By doing this, we will achieve all benefits of fastest recovery, precise recovery and a lower runtime overhead in a single solution. We evaluate our automatic selection algorithm (ASA) approach as an algorithm selector during the runtime of stream processing. Moreover, we also evaluated its efficiency in comparison with the time factor. The experimental results show that our approach is 95% efficient and fast than other conventional manual failure recovery approaches and is hence totally automatic in nature

Awareness of droplet and airborne isolation precautions among dental health professionals during the outbreak of corona virus infection in Riyadh city, Saudi Arabia

Background: This study aimed to determine knowledge, attitude and practice of airborne and droplet isolation precautions among Dental Health Professionals (DHPs) (dental students, interns, practitioners and auxiliaries) during the outbreak of MERS (Middle East Respiratory Syndrome), corona virus infection in Riyadh city, Saudi Arabia. Material and Methods: A cross-sectional survey was conducted among 406 dental health professionals (DHPs) working in selected dental facilities in Riyadh city, Saudi Arabia during the outbreak of MERS (April-June 2013). A structured, close-ended, self-administered questionnaire explored the knowledge, attitude, and practice towards droplet and isolation precautions. Collected data was subjected to descriptive statistics to express demographic information, mean knowledge score, mean attitude score and practice score of DHPs. Inferential statistics (MannWhitney U test and Kruskal Wallis tests, p < 0.05) were used to examine differences between study variables. Spearmanâ s rho correlation was used to identify the association between the knowledge-attitude, knowledge-practice, and attitude-practice. Results: A response rate of rate of 90.22% (406 out of 452) was obtained. The mean scores of knowledge, attitude and practice were 10.61 ± 1.19, 50.54 ± 7.53 and 8.50 ± 2.14 respectively. Spearmanâ s correlation test revealed a significant linear positive correlation between knowledge and attitude (r-0.501, P- 0.01), knowledge and practice (r-0.185, P-0.01) and attitude and practice (r-0.351, P- 0.01) of DHPs about airborne isolation precautions. Conclusions: Dental health professionals considered in the present study showed good knowledge, positive attitude and good practice towards droplet and airborne isolation precautions during outbreak of MERS

Adaptive power control aware depth routing in underwater sensor networks

Underwater acoustic sensor network (UASN) refers to a procedure that promotes a broad spectrum of aquatic applications. UASNs can be practically applied in seismic checking, ocean mine identification, resource exploration, pollution checking, and disaster avoidance. UASN confronts many difficulties and issues, such as low bandwidth, node movements, propagation delay, 3D arrangement, energy limitation, and high-cost production and arrangement costs caused by antagonistic underwater situations. Underwater wireless sensor networks (UWSNs) are considered a major issue being encountered in energy management because of the limited battery power of their nodes. Moreover, the harsh underwater environment requires vendors to design and deploy energy-hungry devices to fulfil the communication requirements and maintain an acceptable quality of service. Moreover, increased transmission power levels result in higher channel interference, thereby increasing packet loss. Considering the facts mentioned above, this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in UWSNs. The contributions of this technique is threefold. First, it uses the adaptive power control mechanism to utilize the sensor nodes’ battery and reduce channel interference effectively. Second, thresholds are defined to ensure successful communication. Third, clustering can be implemented in dense areas to decrease the repetitive transmission that ultimately affects the energy consumption of nodes and interference significantly. Additionally, mobile sinks are deployed to gather information locally to achieve the previously mentioned benefits. The suggested protocol is meticulously examined through extensive simulations and is validated through comparison with other advanced UWSN strategies. Findings show that the suggested protocol outperforms other procedures in terms of network lifetime and packet delivery ratio

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%

Performance Evaluation of a B2C Model Based on Trust Requirements and Factors

This paper evaluates the performance of a newly proposed B2C e-commerce model based on trust factors and requirements in the context of Saudi Arabia. Two categories of trust factors, namely, governmental and nongovernmental types, are identified to create the model for determining the feasibility of an efficient online business strategy in the Kingdom. Data are collected over a duration of 10 weeks based on the designed questionnaire, carefully analyzed, and interpreted. The standpoint of the end user is analyzed to determine the influence of the proposed trust requirements and factors on B2C e-commerce in Saudi Arabia. The reliability of the questionnaires for each requirement with their factors is quantitatively analyzed using Cronbach’s alpha. The questionnaire consists of three parts, namely, demographic component, questions related to the identified requirements, and additional notes as an open question. Questions are designed as per the requirements and the factors of trust models to demonstrate their possible relationship. Furthermore, the questionnaires’ content validity is judged by expert lecturers with relevant specialization before distributing them, which are well organized together with easy understandability. They are randomly distributed among 222 academic and administrative staff (female and male) in addition to university students from the Faculty of Computer Science and Information System in Saudi Arabia. This random selection performed on total 222 responders ensures the statistical accuracy of the sampling. Adaptable government approaches, enactment, rules, insurance of buyer rights, and banking network situation with less web expenses are demonstrated to be significant to e-commerce expansion in the Kingdom. Implementation of the proposed model is believed to augment consumer self-confidence and reliance together with e-commerce growth in Saudi Arabia

A Predictive Checkpoint Technique for Iterative Phase of Container Migration

Cloud computing is a cost-effective method of delivering numerous services in Industry 4.0. The demand for dynamic cloud services is rising day by day and, because of this, data transit across the network is extensive. Virtualization is a significant component and the cloud servers might be physical or virtual. Containerized services are essential for reducing data transmission, cost, and time, among other things. Containers are lightweight virtual environments that share the host operating system&rsquo;s kernel. The majority of businesses are transitioning from virtual machines to containers. The major factor affecting the performance is the amount of data transfer over the network. It has a direct impact on the migration time, downtime and cost. In this article, we propose a predictive iterative-dump approach using long short-term memory (LSTM) to anticipate which memory pages will be moved, by limiting data transmission during the iterative phase. In each loop, the pages are shortlisted to be migrated to the destination host based on predictive analysis of memory alterations. Dirty pages will be predicted and discarded using a prediction technique based on the alteration rate. The results show that the suggested technique surpasses existing alternatives in overall migration time and amount of data transmitted. There was a 49.42% decrease in migration time and a 31.0446% reduction in the amount of data transferred during the iterative phase

A Novel Approach for Securing Nodes Using Two-Ray Model and Shadow Effects in Flying Ad-Hoc Network

In the last decades, flying ad-hoc networks (FANET) have provided unique features in the field of unmanned aerial vehicles (UAVs). This work intends to propose an efficient algorithm for secure load balancing in FANET. It is performed with the combination of the firefly algorithm and radio propagation model. To provide the optimal path and to improve the data communication of different nodes, two-ray and shadow fading models are used, which secured the multiple UAVs in some high-level applications. The performance analysis of the proposed efficient optimization technique is compared in terms of packet loss, throughput, end-to-end delay, and routing overhead. Simulation results showed that the secure firefly algorithm and radio propagation models demonstrated the least packet loss, maximum throughput, least delay, and least overhead compared with other existing techniques and models