Research Trends on the Usage of Machine Learning and Artificial Intelligence in Advertising

Profiling the AI speaker user: Machine learning insights into consumer adoption patterns

The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decision trees, random forests, support vector machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). The final XGboost model, which performed the best among the other machine learning models, specifically forecasts individuals aged 45-50 and 60-65, who are active on social networking platforms and have a preference for varied programming content, as the most likely future users. Additionally, the model reveals their distinct lifestyle patterns, such as higher internet usage during weekdays and increased cable TV viewership on weekends, along with a better understanding of 5G technology. This pioneering effort in IoT consumer research employs advanced machine learning to not just predict, but intricately profile potential AI speaker consumers. It elucidates critical factors influencing technology uptake, including media consumption habits, attitudes, values, and leisure activities, providing valuable insights for creating focused and effective advertising and marketing strategies.

Comparison of the Usability of Apple M2 and M1 Processors for Various Machine Learning Tasks

Thispaper compares the usability of various Apple MacBook Pro laptops were tested for basic machine learning research applications, including text-based, vision-based, and tabular data. Four tests/benchmarks were conducted using four different MacBook Pro models-M1, M1 Pro, M2, and M2 Pro. A script written in Swift was used to train and evaluate four machine learning models using the Create ML framework, and the process was repeated three times. The script also measured performance metrics, including time results. The results were presented in tables, allowing for a comparison of the performance of each device and the impact of their hardware architectures.

Artificial Intelligence (AI) in Advertising

Nowadays, information technology is not only widely used in all walks of life but also fully applied in the marketing and advertisement sector. In particular, Artificial Intelligence (AI) has received growing attention worldwide because of its impact on advertising. However, it remains unclear how social media users react to AI advertisements. The purpose of this study is to examine the behavior of social media users towards AI-based advertisements. This study used a qualitative method, including a semi-structured interview. A total of 23 semi-structured interviews were conducted with social media users aged 18 and over, using a purposive sampling method. The interviews lasted between 27.05–50.39 minutes on average (Mean: 37.48 SD: 6.25) between August and October 2021. We categorized the findings of the current qualitative research into three main process themes: I) reception; II) diving; and III) break-point. While 'reception' covers positive and negative sub-themes, 'diving' includes three themes: comparison, timesaving, and leaping. The final theme, 'break-point', represents the decision-making stage and includes negative or positive opinions. This study provides content producers, social media practitioners, marketing managers, advertising industry, AI researchers, and academics with many insights into AI advertising.

Artificial intelligence in multi-parametric magnetic resonance imaging: A review

Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

Efficient big data security analysis on HDFS based on combination of clustering and data perturbation algorithm using health care database

In this manuscript proposes an efficient big data security analysis on HDFS based on the combination of Improved Deep K-means Clustering (IDFKM) algorithm and Modified 3D rotation data perturbation algorithm using health care database. To compile a similar group of data, an Improved Deep K-means Clustering (IDFKM) Algorithm is used as partitioning the medical data. After clustering, Modified 3D rotation data perturbation technique is used to satisfy the privacy requirement of the client. Modified 3D rotation Data Perturbation technique perturbs each and every sensitive data of the cluster and all the key parameters values used for clustering have warehoused in the database file sector. The proposed approach is executed by Java program, its efficiency is assessed by Health care database. The metrics under the study of memory usage attains higher accuracy 34.765%, 23.44%, 52.74%, 18.74%, lower execution time 35.23%, 23.76%, 27.86%, 27.76%, higher Efficiency 26.85%, 38.97%, 28.97%, 35.65%. then the proposed method is compared with the existing methods such asSecurity Analysis of SDN Applications for Big Data with spoofing identity, Tampering with data, Repudiation threats, Information disclosure, Denial of service and Elevation of privileges (STRIDE), Big Data Analysis-based Secure Cluster Management for using Ant Colony Optimization (ACA) Optimized Control Plane in Software-Defined Networks, System Architecture for Secure Authentication and Data Sharing in Cloud Enabled Big Data Environment using LemperlZivMarkow Algorithm (LZMA) and Density-based Clustering of Applications with Noise (DBSCAN), Big Data Based Security Analytics using data based security analytics (BDSA) approach for Protecting Virtualized Infrastructures in Cloud Computing respectively.

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory (LSTM),-to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer's real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses.

Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow

Standing at the cusp of an augmented age facilitates a glance into the future of a cybernetic world aligned with planetary wellbeing. The era of exponential technological revolutions has brought with it a plethora of opportunities expanding in a multi-faceted dimension with an added emphasis towards nurturing a mutual synergy of nature with a daily dose of digitalization. The paper is written with an intent to lay out an accumulated comprehensive review of different literary works which lay the grounds for how different Smart Earth Technologies aid in monitoring and tackling the degradation of air and water resources. If an intertwined state-of-the-art centralized research source could be created, it would become a boon for seasoned researchers and neophytes succeeding portion of the article expands itself to a wide variety of research literature complimented with real-time models, case, and empirical studies which help heighten the previous limit to the research done on these Technologies tinkering the present monitoring systems. The primary aim of this work is to fuel the need of theoretical, practical, and empirical evolution in the ways the intelligent technologies help blossom a pollution-free environment. The secondary intention was to ensure that in-depth study of Smart Environmental Pollution the Monitoring Systems provisioned a multitude of prospects for upgrading one's knowledge on environmental management through current world technologies. By looking at these trends of the past, the enthusiast of technology could collaborate with the researchers of Environmental Pollution to assist in proliferation of diverse 'smart' solutions creating a Smarter, Greener, and Brighter future for research and developments in Sustainable Technologies devising a pollution-free environment.

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Although the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a "machine" that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.