Social Network Sites as Community Building Tools in Educational Networking

This paper discusses the educational context of social network sites (SNSs) and the manner in which they are adopted as Europeanization tools to develop the main EU education priorities. The Erasmus + (E+) context, articulated on networks of education bodies, denotes a promising ground to investigate social and digital trends emerging within institutionalized education communities. The research approach counts on a sample of 518 organizations aligned to a set of standards regulated within the institutionalized networking frame of the E+ program. Due to the compliance requirements of E+ guidelines, this work proposes a theoretical juxtaposition of the Community of Inquiry framework and the E+ framework. Final results show an EU educational trend verging to informal affordances and non-formal education features.

Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks

Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.

Computational Journalism Analysis on Young Adults' Body Images and Attitudes Toward Plastic Surgery

Computational journalism, especially social media analysis, is a very popular field in computational science. This study was conducted to explore and analyze the impact of the intensity of the exposure to social media on young Thai adults' body images and attitudes toward plastic surgery. The purposive sampling method was used for choosing 250 young Thai men and women aged 21 to 40 who used Facebook and/or Instagram on a regular basis. Online survey questionnaires were posted on Facebook for one month to achieve the results. It was found that young Thai adults frequently and heavily used both social media. Having appearance pressure from and repeated social comparison with idealistic media images, a considerable number of participants displayed more negative self-perceptions and engaged in appearance-changing strategies through increased appearance investment. The results showed that the more these young adults were exposed to social media, the more they were likely to develop a negative body image of themselves, which later caused their attitude toward plastic surgery to be positive.

Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm

Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.

Emerging 5G IoT Smart System Based on Edge-to-Cloud Computing Platform

Security over internet communication has now become difficult as technology is increasingly more effective and faster, particularly in resource limited devices such as wireless sensors, embedded devices, internet of things (IoT), radio frequency identification (RFID) tags, etc. However, IoT is expected to connect billions of computers as a hopeful technology for the future. Hence, security, privacy, and authentication services must protect the communication in IoT. There are several recent considerations, such as restricted computing capacity, register width, RAM size, specific operating environment, ROM size, etc. that have compelled IoT to utilize conventional measures of security. These technologies require greater data speeds, high throughput, expanded power, lower bandwidth, and high efficiency. In addition, IoT has transformed the world in light of these new ideas by offering smooth communication between heterogeneous networks (HetNets).

Wide Band Micro-Strip Antenna Design for Higher “X” Band

The article uncovers a rectangular multi-opening microstrip reception apparatus with wideband working frequencies for the uses of the 'X' band. Grave choice of measurement, the situation of feed, and opening are mulled over lady to accomplish the objective properties of the reception apparatus. Articulate receiving wire execution parameters like return misfortune, voltage standing wave proportion, increase, and effectiveness are determined utilizing the reception apparatus plan programming. Created results are additionally approved with the manufactured receiving standards. Sets of rectangular equal openings are cut on the fix for radiation. The significant recurrence of activity of the reception apparatus is between 11-12GHz, and data transmissions are in the scope of 250 MHz to 2.5 GHz at different frequencies. Discretionally, other execution parts of the receiving antenna are likewise in won understanding. Structured receiving antennae are straightforward, easy, light in weight, and furthermore simple to produce. Union with these elements receiving antennae is adjusted and is likewise having a wide scope of working recurrence.

Energy-Efficient Route Protocols to Minimize Holes in Wireless Sensor Networks Using Probability Enhancement Algorithm

Wireless sensor networks are widely utilized. In the network of wireless sensors, the nodes of sensors normally disseminated arbitrarily are conditional on the method preferred to realize the sensor network. Primarily, the lifespan of a sensor node depends on the active node numbers along with the network connectivity. When a sensor node runs out of power, the sensor node dies too early, affecting network performance. Therefore, an energy hole will be formed with the network. To avoid the problem of energy holes, a number of rules are already proposed. This paper proposed a new method to resolve the problem of energy holes in wireless sensor networks and maximizes the useful life of the network through a different way of cluster head selection using asymmetrical clustering method. This paper proposed PE (probability enhancement) method for choosing the cluster head, which gives improved output compared to LEACH as well as PEGASIS protocol. The result of simulation is performed with MATLAB, and it appears that the projected scheme works better than the previous scheme.

Sentiment Analysis of Healthcare Reviews Using Context-Based Feature Weight Embedding Technique

Healthcare reviews play a major role in providing feedback to consumers as well as medical care information to users. Historically, the sentiment analysis of clinical documents will help patients in analyzing the medicines and identifying the relevant medicines. Existing methods of word embeddings use only the context of words; hence, they ignore the sentiment of texts. Medical review analysis is important due to several reasons. Patients will know the results of using medicines since such information is not easily obtained from any other source. Historical results of predictive analysis say that among people aged 55-80, the death rate from 2005 to 2015 in the US was at the top for the deadliest disease, which increased exponentially. Traditional machine learning techniques use a lexical approach for feature extraction. In this paper, baseline algorithms are checked with the proposed work of the recurrent network, and results show that the method outperforms baseline methods by a significant improvement in terms of precision, recall, f-score, and accuracy.

Unscented Particle Filter Approach for Underwater Target Tracking

This research aims to find an appropriate approach to improve system accuracy in the Doppler-bearing tracking (DBT) problem for target estimation. The topic of DBT problem is to achieve a target trajectory using bearing and frequency measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. The unscented particle filter approach is proposed to estimate the accuracy in the target motion parameters (TMP). This approach requires the observer maneuver so that the target trajectory is observable. Although in recent research papers, DBT has been proven to work efficiently without observer maneuver, TMP is unknown to the observer, and consequently, there is a need for observer maneuver. So, the algorithm is simulated with observer following s-maneuver and without any maneuver executed by the observer, and results are compared. The effectiveness of the solution and results are determined by using MATLAB simulation. It is shown that the truthfulness of the outcome is superior when the observer performs s-maneuver while compared to that without observer maneuver.

Design and Development of Customer Relationship Management Recommendations by Clustering and Profiling of Customers Using RFM

RFM (recency, frequency, monetary) examination is a method to perceive high-response customers in promoting progressions and to improve general response rates, which is remarkable and is comprehensively associated today. Less extensively understood is the estimation of applying RFM scoring to a customer database and evaluating customer advantage. A customer who has passed by an e-keeping cash site recently (R) and frequently (F) and influenced a huge amount of monetary to esteem (M) through portion and standing solicitations is presumably going to visit and make portions yet again. After appraisal of the customer's lead using specific RFM criteria, the RFM score is related to the bank excitement, with a high RFM score being more important to the bank by and by and later on.