Impact of Facebook and Newspaper Advertising on Sales: A Comparative Study of Online and Print Media

Is Trust in Information Sources Associated with Drug Use? A Population-Based Study

Aim

Communication and information sources can play an important role when addressing drug use. The aim of this study is to assess the association of different levels of trust in information sources regarding drug use within different population groups.

Methods

Data was gathered using a mixed methods approach, with an online survey and interviews. A structured questionnaire was designed for data collection using the methodology of the European Monitoring Centre for Drugs and Drug Addiction, with additional items measuring trust in the information sources.

Results

In total 9,161 inhabitants of Slovenia aged 15-64 years and living in the private households completed the survey as part of this non-experimental quantitative study (response rate: 57%). A total of 20.7% of the participants reported having used cannabis or hashish at least once in their lives, 2.5% cocaine/crack cocaine and 0.4% heroin. Mean age of the first cannabis/hashish use was 19.59 years, cocaine/crack cocaine 22.73 years and heroin 20.63 years. The participants most value and trust the information sources regarding tobacco, alcohol and illicit drugs if it comes from healthcare workers or immediate family and other relatives, and put the least trust in the internet and television.

Conclusions

The data show that drug users have less trust in the given information sources compared to the whole sample. The present research serves as evidence for development and implementation of targeted interventions, including communication activities and tools.

Overview of 5G & Beyond Security

Network security is a crucial concern when it comes to computation, concerns like threats can have high consequences, and critical information will be shared with unauthorized persons. This paper presents a detailed survey on Fifth Generation (5G) and security aspect. This is more predictable since the core technology; the synonymous approach is possible with Fifth Generation (5G) and Beyond Technologies though with limited access. Many incidents have shown that the possibility of a hacked wireless network, not just impacts privacy and security worries, but also hinders the diverse dynamics of the ecosystem. Security attacks have grown in frequency and severity throughout the near past, making detection mechanisms harder.

Recommendation System Comparative Analysis: Internet of Things aided Networks

Today, the public is not willing to spend much time identifying their personal needs. Therefore, it needs a system that automatically recommends customized items to customers. The Recommender system has an internet of things (IoT) that entails a subclass of evidenced-based sieving structures that pursues to forecast the assessment of a customer would stretch to an item. Within social networks, numerous categories of RS operate on different recommendation expertise. In this state-of-the-art, we describe and classify current studies from three different aspects by describing different methods of recommender systems. The Friend Recommendation System in social networks is necessary and inevitable, and it is due to this kind of coordination that inevitably recommends latent friends to customers. Making recommendations for friends is an imperative assignment for community networks, as obligating supplementary networks customarily superiors to enhanced customer experience.

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

Machine learning (ML) entails artificial procedures that improve robotically through experience and using data. Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML. This study mainly focuses on RL and Deep learning, since necessitates mainly sequential and consecutive decision-making context. This is a comparison to supervised and non-supervised learning due to the interactive nature of the environment. Exploiting a forthcoming accumulative compensation and its stimulus of machines, complex policy decisions. The study further analyses and presents ML perspectives depicting state-of-the-art developments with advancement, relatively depicting the future trend of RL based on its applicability in technology. It's a challenge to an Internet of Things (IoT) and demonstrates what possibly can be adopted as a solution. This study presented a summarized perspective on identified arenas on the analysis of RL. The study scrutinized that a reasonable number of the techniques engrossed in alternating policy values instead of modifying other gears in an exact state of intellectual. The study presented a strong foundation for the current studies to be adopted by the researchers from different research backgrounds to develop models, and architectures that are relevant.

Marketing Strategies in the Banking Services Sector With the Help of Data Science

Competition between marketing strategies of enterprises shifts to the use of artificial intelligence and begins to be considered in the context of competition between Data Science projects. Therefore, the issue of developing methodology and building a model in a particular area is relevant, which will make the project quite effective and ensure the achievement of goals for the company. The banking services market has a certain specificity of consumer behaviour, so forming marketing strategies is a somewhat complex process. Thus, banks face the task of maintaining the loyalty of their existing customers and attracting new ones. This article aims to build a marketing strategy to attract new customers in the banking sector using Data Science tools. The result of the study is the construction of two econometric models of the different bank's credit products: cash loans and credit cards, which determine the influence of various factors on this process and helps to distribute the advertising budget between different types of advertising. Using the built model, it was determined that advertising campaigns directly affect the increase in the number of new customers in the bank and the overall growth of brand knowledge about the banking institution in society. In addition, the determined weights of each influencing factor helped form an advertising budget, which increased customer inflows by 12%, with an average ROI of 3.18. Taking all into account, the model had shown its effectiveness in organising the bank's advertising campaign when decisions were made using Data Science technologies. The results obtained based on the models give a fairly clear understanding of the factors influencing the inflow of new customers in the bank, which will model the distribution of the budget for advertising campaigns in future periods and predict their effectiveness. Competition in the country's financial sector is forcing banking institutions to use data science in their marketing activities.