Era Of Artificial Intelligence Research Articles

Digital Marketing in the Age of Artificial Intelligence: Challenges, Opportunities, Trends

The article examines the main trends in marketing according to leading digital agencies in Bulgaria. Analysis of existing publications shows that artificial intelligence retains its dominant role in digital marketing. Without a doubt, AI provides marketers with new and effective ways to target audiences, personalize content and measure results. At the same time, already established and widely used marketing tools such as email marketing and content marketing continue to be a leading element in brand strategies for reaching relevant audiences. As a result of the content analysis, six key trends have been identified predetermining the development of marketing, developed as a priority in a digital environment: artificial intelligence, personalization, email marketing, the social network TikTok, video content and sustainability as a way of production, consumption and communication. In the context of the so-called never normal, reflecting the uncertainty and dynamics of the complex global reality, the knowledge and application of the challenges and opportunities reflected in the article in the era of artificial intelligence imply the achievement not only of competitiveness and significant success but also of higher sustainability for companies.

Innovative teaching methodologies in the era of artificial intelligence: A review of inclusive educational practices

Artificial intelligence (AI) is revolutionizing the field of education, offering new opportunities to enhance learning experiences and promote inclusive educational practices. This review explores the impact of AI on teaching methodologies and its role in creating inclusive learning environments. By examining current research and practices, this review highlights the potential of AI to address diverse learning needs and promote equity in education.The review begins by discussing the role of AI in personalized learning, where AI algorithms analyze student data to provide tailored instruction and feedback. This approach allows educators to cater to individual learning styles and preferences, ensuring that all students have access to high-quality education. Additionally, AI-driven adaptive learning systems can identify and address learning gaps, providing targeted interventions to support students who may be struggling. Furthermore, the review explores the use of AI in facilitating collaborative learning environments, where students work together on projects and tasks. AI technologies can enhance collaboration by providing tools for communication, coordination, and knowledge sharing. This approach promotes inclusivity by allowing students to contribute their unique perspectives and skills to group projects. The review also discusses the potential of AI in promoting accessibility and inclusivity for students with disabilities. AI-powered assistive technologies can provide additional support and accommodations, allowing students with disabilities to fully participate in educational activities. Additionally, AI-driven captioning and translation tools can improve accessibility for students who are deaf or hard of hearing, as well as those who speak languages other than the primary language of instruction. Overall, this review highlights the transformative potential of AI in education and its ability to promote inclusive educational practices. By leveraging AI technologies, educators can create more personalized, collaborative, and accessible learning environments, ensuring that all students have the opportunity to succeed.

A Review on Deep Learning For Electroencephalogram Signal Classification

Recently, the research on Electroencephalogram (EEG) signals have been obtained more focus at the same time the EEG signal is regarded as the basis for the prediction of diagnosis disease and the brain behavior. EEG is as significant tool for many conditions that can be recorded the brain human waves which accommodate the brain activity. In the recent decades, EEG data has been extensively applied in the approaches of data analysis such as time series analysis. With the considerable achievement of deep learning (DL) implement on the time series data, multiple studies have been began applying deep learning algorithms on the processing of EEG signal. Several deep learning techniques that assistant in the detection various psycho-neuro disorders, have been proposed in order to automate EEG detection and classification with great development in multiple applications of EEG signals. Also, different machine learning (ML) algorithms have been presented in such research for brain signals identification and classification in the era of Artificial intelligence (AI). In an attempt to summarize the EEG signal processing techniques, we have performed a literature review around deep learning algorithms for decoding the human’s brain activity as well as diagnosis disease and clarified particulars about several deep learning algorithms. We also conducted some of ML papers about EEG signals classification. Based on the achievement results of the research mentioned in this article appears an advanced scientific development in terms of deep learning.

Realism Reimagined: Navigating Socialist Realism from Marx to the Digital Era

This article comprehensively examines the pivotal logical junctures and developmental milestones of Socialist Realism, offering an in-depth elucidation of its essence, identification of inherent issues, and a nuanced analysis of the causal factors contributing to these challenges. Subsequently, the study endeavours to explore the often-overlooked potential of Socialist Realism within the contemporary landscape characterized by cultural digitization and rapid advancements in artificial intelligence. In this context, the article proposes a revaluation of Socialist Realism, highlighting its potential resurgence and novel guiding significance in shaping cultural narratives in the era of artificial intelligence.

Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering

Remote sensing, specifically satellite imagery, is gaining prominence in computer science nowadays, in the era of artificial intelligence, in an attempt to deliver more precise information. The satellite images of Earth are gathered, evaluated, and processed for use in civil and military applications with a military aim. Satellite images do have a wide range of services. The areas of study of agriculture, fishery, oceanography, and meteorology include geology, biodiversity, cartography, land use planning, and armed conflict. Transformation is the goal of the categorization of satellite images. Transformation of satellite images into information that can be used rather than having an image of a location. This paper classified a scene of the Landsat-8 satellites with specifications (Path=168 and Row=38). This scene was classified into four categories (Water, Vegetation, bare land, and Build-up) based on the unsupervised classification method (ISO Clustering). The ISO Clustering method is found in the Arc Map program. The results regarding classification accuracy are a good percentage compared to unsupervised Classification.

Enhancing SME Product Brand Equity in The Digital Age as Strategic Approaches in the Era of Artificial Intelligence

This research explores the role of strategic approaches in enhancing Small and Medium-sized Enterprises' (SMEs) product brand equity in the digital era. It focuses on the integration of Artificial Intelligence (AI) technologies, highlighting the opportunities and challenges SMEs face in enhancing their brand equity. The study evaluates various strategies for SMEs to navigate the digital complexities, including machine learning algorithms, natural language processing, and predictive analytics, to refine brand positioning, customer engagement, and personalized experiences. It also explores ethical considerations and potential risks associated with AI adoption, offering insights into maintaining consumer trust and ethical practices. The research synthesizes theoretical frameworks with practical implications, contributing to the understanding of strategic paradigms for SMEs to thrive in the digital age. The findings aim to provide actionable insights for SMEs and stakeholders to craft robust strategies aligning with AI capabilities, fostering sustainable brand equity growth and competitive advantage.

Subjective or Objective: A Study of Classifying News Content based on Machine Learning Algorithms

In today's world, self-media as well as streaming media platforms have gradually become the main channels for news dissemination. With the advent of the artificial intelligence era, AI-generated technology will profoundly affect the news industry. Many press releases generated by AI are sufficiently "fake" as well as cascading, so the subjectivity and objectivity of news media are gradually blurred. In order to better standardize and regulate the articles published on self-media platforms, this study is based on 1,000 sports articles and machine learning algorithms to categorize and describe the content of the articles, so as to determine whether the articles are objective or not. At the end of the study, we found that the KNN algorithm performed best in classification.

Advancements in Precision Agriculture: Integrating Computer Vision for Intelligent Soil and Crop Monitoring in the Era of Artificial Intelligence

Precision Agriculture has witnessed significant advancements with the integration of computer vision and artificial intelligence (AI) technologies, marking a transformative era in modern farming practices. This research explores the synergies between computer vision and intelligent soil and crop monitoring in the context of precision agriculture. The study aims to contribute insights into the application of advanced technologies for optimizing agricultural processes, enhancing resource efficiency, and improving overall crop yield. Keywords— Precision Agriculture, Computer Vision, Artificial Intelligence, Soil Monitoring, Crop Monitoring, Image Processing, Machine Learning, Deep Learning, Agricultural Technology, Intelligent Farming, Data Analytics, Precision Farming, Sensor Technologies, Agricultural Innovation, Sustainable Agriculture.

Medical Research on the Impact of Sports on College Students Mental Health in the Era of Artificial Intelligence

Medical Research on the Impact of Sports on College Students Mental Health in the Era of Artificial Intelligence

Exploring ChatGPT for next-generation information retrieval: Opportunities and challenges

The rapid advancement of artificial intelligence (AI) has spotlighted ChatGPT as a key technology in the realm of information retrieval (IR). Unlike its predecessors, it offers notable advantages that have captured the interest of both industry and academia. While some consider ChatGPT to be a revolutionary innovation, others believe its success stems from smart product and market strategy integration. The advent of ChatGPT and GPT-4 has ushered in a new era of Generative AI, producing content that diverges from training examples, and surpassing the capabilities of OpenAI’s previous GPT-3 model. In contrast to the established supervised learning approach in IR tasks, ChatGPT challenges traditional paradigms, introducing fresh challenges and opportunities in text quality assurance, model bias, and efficiency. This paper aims to explore the influence of ChatGPT on IR tasks, providing insights into its potential future trajectory.

Effects of exergames on student physical education learning in the context of the artificial intelligence era: a meta-analysis

Whether the application of exergames in physical education (PE) courses can significantly improve student performance in PE learning is still controversial. This review explores the promoting effect of exergames on student PE learning and the conditions in which the effect of exergames can be maximized. Based on the PICOS method, two researchers independently searched the ProQuest database, EBSCO database, Web of Science (WoS) database, PubMed database, Chinese National Knowledge Infrastructure (CNKI) database, Wanfang database, and VIP database, evaluated the literature quality using the Cochrane system evaluation manual, and performed a meta-analysis of the included literature. A total of 16 randomized controlled trials involving 2962 subjects were included in this study. The meta-analysis showed that exergames effectively improved student performance in PE learning (SMD = 0.45, 95% CI: 0.27–0.63, P < 0.00001). Subgroup analysis indicated that better results could be achieved when exergames were introduced in small kindergarten classes and continued for 1–2 months.

Research on the Challenge and Response Strategy of Labor Employment in the Era of Artificial Intelligence

The rapid development of information technology has led the world into the era of artificial intelligence. This technological change has a profound impact on the social and economic structure, not only changing people's lifestyle, but also a significant impact on the labor and employment market. The extensive application of artificial intelligence technology has promoted the development of the industry, providing new opportunities for Chinese workers, but at the same time, it has also led to the intensification of large -scale unemployment and structural contradictions in the labor market and the expansion of income gap. In order to effectively respond to these challenges, it is recommended that the state improve the social security system, promote employment and transfer training of unemployed personnel, adjust and optimize the industrial structure, cultivate new growth points of artificial intelligence employment, and strengthen the education and training of artificial intelligence technology, improve the workers Professional skills and literacy to achieve the goal of high -quality employment.

Abstract 1303: Harness the power of data to improve cancer care - Understanding the complex landscape of health policies and regulations

Abstract In the era of big data and artificial intelligence, biomedical research and healthcare have been presented with unprecedented opportunities for improving the understanding of cancer biology. In addition, the Open Science Initiative aims to shift the current paradigm of research practice towards broad sharing and public access to research findings as the norm. Big data in oncology usually includes a combination of genomic sequencing, proteomics, medical imaging, electronic health records, payor records, and other data sources from pharmaceutical research, wearables, and medical devices. While combining, linking, and analyzing data from different sources across research and healthcare enterprises is key to realizing the promise of big data and a health learning system, it poses tremendous challenges for researchers and implementers to understand the nuances of relevant policies, laws, and regulations while striving for broad, equitable, timely and responsible data sharing, as well as tackling issues in data quality, harmonization, interoperability, and security. Herein, we aim to shine a light on the ever-evolving landscape of data privacy regulations, laws, and policies that govern different touch points of a patient's journey where data are collected for research and healthcare purposes throughout the lifecycle that would impact data-sharing outcomes. We hope the illumination of a complex landscape of data governance framework to consider for many data sources will facilitate broad community engagement including data scientists and technologists, patients and advocacy groups, and policymakers to maximize open data and science while protecting patient privacy and confidentiality. Citation Format: Ying Huang, Heather Basehore, Emily S. Boja, Jaime M. Guidry Auvil. Harness the power of data to improve cancer care - Understanding the complex landscape of health policies and regulations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1303.

From McCulloch to GPT - 4: stages of development of artificial intelligence.

The article examines the history of the development of artificial intelligence (AI), starting from its first theoretical and practical steps and tracing the evolution to modern achievements. The article provides an overview of the key milestones, scientific discoveries and technological breakthroughs made in the field of AI. The most important figures, ideas and principles that influenced its development are also discussed. In the context of this development, various definitions of artificial intelligence are given. There are several key stages in the history of AI: the early stages, the quiet period, the AI renaissance, and the era of AI in the new millennium. Each of these stages made its own unique contribution to the progress of AI. The modern period is characterized by rapid development, especially in the field of machine learning and deep learning. These methods allow artificial intelligence to learn from data and identify complex patterns. Advances in natural language processing, such as models GPT and its modifications, have shown outstanding results. However, despite linguistic advances, GPT remains limited in aspects important to creating strong AI. The article discusses the limitations of modern language models, as well as the prerequisites and prospects for the development of strong artificial intelligence. Special attention is paid to the project of Elon Musk, who, having launched the company X.AI, is engaged in research in the field of creating strong AI with the goal of “knowledge of reality.” The article also proposes an alternative approach to creating strong artificial intelligence - the development of an artificial brain based on a multidimensional multi-connected receptor-effector neuron-like growing network. Some aspects of the emergence of artificial consciousness are also considered.

Research on the Path of Higher Vocational Talent Training in the New Era under the Background of Artificial Intelligence

With the advent of the era of artificial intelligence, it is difficult to rely on standardized education, and the development of The Times puts forward new requirements for talent training. The talents trained by higher vocational education in the new era should have the characteristics of growth mentality and high consciousness learning, human-machine symbiotic thinking and AI penetration skills, pioneering spirit and the ability to "break boundaries", the creative ability and practical wisdom, the combination of humanistic spirit and science and technology, human community thinking and cross-cultural action. Therefore, it is proposed that higher vocational education should focus on the target object of talent training in higher vocational colleges in the intelligent era, build a digital twin Sunac platform, form a new technology base, and develop new skills with the integration of industry and education certification. We are committed to cultivating the fertile soil for the growth of talents, and enabling the continuous emergence of high-quality skilled talents in the new era.

Negative Alienation of Artificial Intelligence and Its Solution

In the era of artificial intelligence, the negative alienation of artificial intelligence has become increasingly prominent, which has delayed the development of human beings to a certain extent. The main reason is that human beings gradually ignore their own subjective initiative and lack rational understanding of human civilization, which is a problem that human beings need to face and solve. From the perspective of philosophy, to overcome the negative alienation of artificial intelligence, it is necessary to clarify the dominant position of human beings and rationally understand human civilization, so as to better promote the harmonious development of human beings and artificial intelligence, nature and society.

Cui bono? Judicial decision-making in the era of AI: A qualitative study on the expectations of judges in Germany

Despite substantial artificial intelligence (AI) research in various domains, limited attention has been given to its impact on the judiciary, and studies directly involving judges are rare. We address this gap by using 20 in-depth interviews to investigate German judges’ perspectives on AI. The exploratory study examines (1) the integration of AI in court proceedings by 2040, (2) the impact of increased use of AI on the role and independence of judges, and (3) whether AI decisions should supersede human judgments if they were superior to them. The findings reveal an expected trend toward further court digitalization and various AI use scenarios. Notably, opinions differ on the influence of AI on judicial independence and the precedence of machine decisions over human judgments. Overall, the judges surveyed hold diverse perspectives without a clear trend emerging, although a tendency toward a positive and less critical evaluation of AI in the judiciary is discernible.

Study on Evaluation of Execution Capability Based on Artificial Intelligence CIPP Model

INTRODUCTION: The rapid change in artificial intelligence has evaluated ideological and political education ability in colleges and universities as a significant challenge.OBJECTIVES: To assess the level of competence of universities in ideological and political education to determine the effectiveness and efficacy of educational programs and to provide a basis for improving and upgrading academic competence.METHODS: Based on the CIPP model, the author constructed an index system and selected a suitable evaluation model to conduct a study on the evaluation of ideological and political competence of colleges and universities in the context of Artificial Intelligence, which helps to understand the background conditions, resource allocation, teaching activities and quality of teaching of educational programs, as well as the level of ideological and political literacy of the students and their achievements.RESULTS: The evaluation results show that this kind of evaluation research helps to improve and enhance the capacity of ideological and political education in colleges and universities, and at the same time, it can dig into the implementation effect of the educational program, find problems and shortcomings, and promote the continuous improvement of the educational program.CONCLUSION: Through evaluation, the quality and level of ideological and political education in colleges and universities can improve students' ideological and political literacy and sense of social responsibility. In addition, based on this, it makes the development of ideological and political ability in colleges and universities can be better adapted to the era of artificial intelligence.

The health technology assessment in the artificial intelligence era: the AI surgical department

The health technology assessment in the artificial intelligence era: the AI surgical department

Understanding the Mechanism of Conducting Benchmark Test for the Infrastructure of Physical Education Curricula in the Age of Artificial Intelligence

The study aims to propose a vision for creating a standardized test according to the development of physical education curricula in the era of artificial intelligence, so that we can be able to measure student achievement and master the new curriculum standards. Based on the results, the following can be concluded: directing the teaching and learning process, and determining levels. For students, students’ teaching performance, students’ level of understanding and knowledge, application and achievement of lesson objectives in physical education. There is a strong correlation between most of the skills, knowledge, and behaviors that were measured in the study. This suggests that these skills, knowledge, and behaviors are related to each other, and that students who are high in one are more likely to have a high level in the other skills, knowledge, and behaviors. This is due to the fact that the students who participated in the study had a good level of performance in all the criteria that were measured. Based on the results, the following recommendations can be made: Focus on developing teaching skills and interaction with students, as they are closely related to the level of understanding and knowledge of students. Focus on developing knowledge and understanding of mathematical concepts, mathematical skills, and sporting values and behaviors’, as these skills and knowledge are closely linked to the application and achievement of lesson objectives in physical education.