• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.120-126
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• 2021

The article analyzes the possibility of using information and innovation experience in training public administration specialists, and also explores the system of training public administration and management specialists abroad. It was determined that in the European Union, Japan and other developed countries, three concepts of qualified personnel training will be developed: the concept of specialized training is focused on the present or near future and is relevant for the respective workplace; the concept of multidisciplinary training is effective from an economic point of view, as it increases intra-production and non-production mobility of an employee; the concept of learner-centered learning with the aim of developing human qualities.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.212-218
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• 2021

Low-power and Lossy Networks (LLNs) have become the common network infrastructure for a wide scope of Internet of Things (IoT) applications. For efficient routing in LLNs, IETF provides a standard solution, namely the IPv6 Routing Protocol for LLNs (RPL). It enables effective interconnectivity with IP networks and flexibly can meet the different application requirements of IoT deployments. However, it still suffers from different open issues, particularly in large-scale setups. These include the node unreachability problem which leads to increasing routing losses at RPL sink nodes. It is a result of the event of memory overflow at LLNs devices due to their limited hardware capabilities. Although this can be alleviated by the establishment of multi-sink topologies, RPL still lacks the support for effective load balancing among multiple sinks. In this paper, we address the need for an efficient multi-sink load balancing solution to enhance the performance of PRL in large-scale scenarios and alleviate the node unreachability problem. We propose a new RPL objective function, Multi-Sink Load Balancing Objective Function (MSLBOF), and introduce the Memory Utilization metrics. MSLBOF enables each RPL node to perform optimal sink selection in a way that insure better memory utilization and effective load balancing. Evaluation results demonstrate the efficiency of MSLBOF in decreasing packet loss and enhancing network stability, compared to MRHOF in standard RPL.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.167-174
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• 2022

Vehicular Ad hoc Networks are considered as special kind of Mobile Ad Hoc Networks. VANETs are a new emerging recently developed, advanced technology that allows a wide set of applications related to providing more safety on roads, more convenience for passengers, self-driven vehicles, and intelligent transportation systems (ITS). Delay Tolerant Networks (DTN) are networks that allow communication in the event of connection problems, such as delays, intermittent connections, high error rates, and so on. Moreover, these are used in areas that may not have end-to-end connectivity. The expansion from DTN to VANET resulted in Vehicle Delay Tolerant Networks (VDTN). In this approach, a vehicle stores and carries a message in its buffer, and when the opportunity arises, it forwards the message to another node. Carry-store-forward mechanisms, packets in VDTNs can be delivered to the destination without clear connection between the transmitter and the receiver. The primary goals of routing protocols in VDTNs is to maximize the probability of delivery ratio to the destination node, while minimizing the total end-to-end delay. DTNs are used in a variety of operating environments, including those that are subject to failures and interruptions, and those with high delay, such as vehicle ad hoc networks (VANETs). This paper discusses DTN routing protocols belonging to unicast delay tolerant position based. The comparison was implemented using the NS2 simulator. Simulation of the three DTN routing protocols GeOpps, GeoSpray, and MaxProp is recorded, and the results are presented.

• Journal of Korean Society of Disaster and Security
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• v.15 no.1
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• pp.23-34
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• 2022

Korea Government promotes the installation of buffering retention facility to protect the water environment and prevent the environmental contamination event by toxic materials. Although the buffering retention facility is very important to protect the natural environment through the retention of accidental pollution and initial runoff, much study has not been done to suggest the guide line for the design of the facility. In this study, we suggested the rational items for the design of buffering retention facility based on many experts after we investigated and compared the previous results of many design materials.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.368-372
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• 2021

The article identifies the features of the digital culture of modern society in the dynamics of its impact on the education sector, identifies the main directions of digitalization education, an objective analysis is presented, the possibilities of examination as a scientific assessment are determined "Digital reforms" of education, the role of traditional values of educational culture in expertise and improvement digital innovations in the education system, identified the main contradictions in the development of digital culture, to determine the directions of its improvement. The article describes the three main components of information technology as a complex of hardware, software and a system of organizational and methodological support; the description of analog and digital information technologies is presented. The authors list the most common multifunctional office applications and IT tools; the advantages of using IT in the educational process are highlighted.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.173-181
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• 2021

The Ministry of Education in Saudi Arabia encourages Saudi students to continue their education at Saudi universities or abroad. Currently, an estimated 1,282,140 Saudi students are studying at Saudi universities. The extent of the research so far has not focused on Saudi student experiences, but it has shown that even a single negative event can dramatically reduce the chances of a student completing a degree. Thus, more research is necessary to identify and describe the context and obstacles (environmental and psychological) that Saudi students face. The evaluation was multifaceted to capture not only performance outcomes, but also other factors that have been suggested by research as influential to students' ability, such as the environmental, cultural, and psychological risks for graduation that Saudi students self-report. A single group pretest (survey) design was used in this study. Findings suggest depression stress and college stress predict stress levels, while subjective happiness predicts levels of scientific participations of the sample. Moreover, depression stress shows more consistency with hours spent on the internet for study purposes. These results should be considered in study support programs both institutionally and geopolitically by universities and governments.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.305-316
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• 2021

The objective is to recognize the better opportunities from targeted reveal advertising, to show a banner ad to the consumer of online who is most expected to obtain a preferred action like signing up for a newsletter or buying a product. Discovering the most excellent commercial impression, it means the chance to exhibit an advertisement to a consumer needs the capability to calculate the probability that the consumer who perceives the advertisement on the users browser will acquire an accomplishment, that is the consumer will convert. On the other hand, conversion possibility assessment is a demanding process since there is tremendous data growth across different information dimensions and the adaptation event occurs infrequently. Retailers and manufacturers extensively employ the retail services from internet as part of a multichannel distribution and promotion strategy. The rate at which web site visitors transfer to consumers is low for online retail, out coming in high customer acquisition expenses. Approximately 96 percent of web site users concluded exclusive of no shopper purchase[1].This category of conversion rate is collected from the advertising of social media sites and pages that dataset must be estimating and assessing with the concept of big data clustering, which is used to group the particular age group of people along with their behavior. This makes to identify the proper consumer of the production which leads to improve the profitability of the concern.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.73-80
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• 2021

Nowadays microblogs have become the most popular platforms to obtain and spread information. Twitter is one of the most used platforms to share everyday life event. However, rumors and misinformation on Arabic social media platforms has become pervasive which can create inestimable harm to society. Therefore, it is imperative to tackle and study this issue to distinguish the verified information from the unverified ones. There is an increasing interest in rumor detection on microblogs recently, however, it is mostly applied on English language while the work on Arabic language is still ongoing research topic and need more efforts. In this paper, we propose a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect rumors on Twitter dataset. Various experiments were conducted to choose the best hyper-parameters tuning to achieve the best results. Moreover, different neural network models are used to evaluate performance and compare results. Experiments show that the CNN-LSTM model achieved the best accuracy 0.95 and an F1-score of 0.94 which outperform the state-of-the-art methods.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.13-30
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• 2023

Since the COVID-19 vaccine became available, people have been sharing their opinions on social media about getting vaccinated, causing discussions of the vaccine to trend on Twitter alongside certain events, making the website a rich data source. This paper explores people's perceptions regarding the COVID-19 vaccine during certain events and how these events influenced public opinion about the vaccine. The data consisted of tweets sent during seven important events that were gathered within 14 days of the first announcement of each event. These data represent people's reactions to these events without including irrelevant tweets. The study targeted tweets sent in Arabic from users located in Saudi Arabia. The data were classified as positive, negative, or neutral in tone. Four classifiers were used-support vector machine (SVM), naïve Bayes (NB), logistic regression (LOGR), and random forest (RF)-in addition to a deep learning model using BiLSTM. The results showed that the SVM achieved the highest accuracy, at 91%. Overall perceptions about the COVID-19 vaccine were 54% negative, 36% neutral, and 10% positive.