Review Of Artificial Intelligence Research Articles

AI in Social Media Marketing : Opportunities and Challenges

Using artificial intelligence (AI) technology to improve brand engagement, targeting accuracy, and content personalisation, social media marketing (SMM) has undergone tremendous evolution. In order to highlight the transformational power of AI through computer vision, natural language processing (NLP), and predictive analytics, this article investigates how AI is integrated into SMM. Artificial intelligence (AI) has revolutionised the way businesses engage with their consumers by enabling more granular customer data, tailored marketing strategies, and streamlined operations. Even with its benefits, AI in SMM has drawbacks such algorithmic bias, data privacy, and ethical issues. This paper offers a thorough review of AI's involvement in SMM, highlighting both opportunities and challenges, by looking at successful implementations and lessons learnt from mistakes.

The review of AI and cultural heritage protectionTaking the whole process of cultural heritage protection as an example

This article aims to summarize the application of artificial intelligence (AI) in cultural protection, taking the entire process of cultural heritage protection as an example. In each process, AI has its own prominent applications. The process of cultural heritage protection is divided into the archaeology of cultural relics, classification, identification, and restoration of cultural relics, cultural heritage management after information identification, and finally, presentation to the public, which is also a part of cultural protection. With the development of technology, AI has participated in various aspects of society. While this study encourage the scientific protection of cultural heritage,this studymust also be vigilant about the negative impact of AI on cultural heritage protection. Through the establishment of regulations and related implementation, this studywill jointly advance the application of AI in the field of cultural heritage protection into a new era.

Review of AI in Civil Engineering

This paper reviews the transformative impact of Artificial Intelligence (AI) on civil engineering. It explores AI's fundamental concepts and its applications across structural analysis, construction management, transportation, geotechnical engineering, and sustainability. The review highlights AI's role in automating tasks, predicting outcomes, and optimizing designs throughout project lifecycles. Recent advancements in AI-driven technologies for structural health monitoring, predictive maintenance, and risk assessment are discussed, along with challenges like data quality and model interpretability. Future trends such as autonomous construction and digital twins are examined, emphasizing the need for continued research and interdisciplinary collaboration. In conclusion, this paper offers insights for leveraging AI to address evolving challenges and opportunities in civil engineering, fostering innovation, sustainability, and resilience in infrastructure development.

A Review of AI in Breast Cancer Detection

Cancer stands out as one of the most pressing global health challenges, and over the past decade, significant advancements have been made in diagnostic tests and methodologies. These tests fall into categories such as imaging tests, and endoscopic procedures, generating substantial volumes of data. This data needs expert evaluation to distinguish between benign and malignant tumors. Enter artificial intelligence (AI), which offers improved accuracy in analyzing large quantities of diagnostic imaging, thereby enhancing the efficiency of healthcare systems. The integration of new AI algorithms, technical advances, and enhanced computer hardware enables the training of diagnostic neural networks. This allows machines to learn from a diverse range of scans, leading to a comprehensive understanding of cancer scanning data. AI software has been evaluated against conventional diagnostic tools used by cancer specialists, and the results show significantly increased precision, making it highly effective in early diagnosis and extended forecasting for various types of cancers. In the realm of breast cancer prognosis, AI systems have demonstrated the potential to surpass human specialists, enabling much earlier diagnosis. Similarly, informatics has developed AI algorithms and deep learning techniques capable of predicting individuals' likelihood of developing lung cancer through low-dose CT analysis. The use of convolutional neural networks (CNNs) has been instrumental in diagnosing the invasion depth of gastric cancer based on gastric endoscopy.

Review of AI and machine learning applications to predict and Thwart cyber-attacks in real-time

The contemporary cybersecurity landscape demands innovative solutions to combat the relentless evolution of cyber threats. Traditional approaches are facing unprecedented challenges, compelling a paradigm shift towards the integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper meticulously explores the potential of AI and ML to fortify real-time cybersecurity, with a focus on the swift prediction and mitigation of cyber-attacks. Against the backdrop of an escalating threat landscape, this paper propels the inquiry into advanced technologies to fortify cybersecurity. The limitations of traditional methodologies underscore the urgency of investigating the efficacy of AI and ML in reinforcing defense mechanisms. This paper endeavors to comprehensively investigate the role of AI and ML in real-time cybersecurity. It places a distinct emphasis on their potential to predict and thwart cyber-attacks promptly. The exploration encompasses diverse dimensions, ranging from the intricacies of model complexity to crucial considerations in security, ethics, and emerging trends. Structured around a robust framework, the exploration encompasses comprehensive research directions. These include the imperative to enhance explainability, address vulnerabilities to adversarial attacks, foster collaboration between humans and AI, and develop quantum-resistant cryptographic solutions. The paper navigates through the intricate technical, organizational, and ethical dimensions inherent in the implementation of AI and ML in real-time cybersecurity. The findings of this exploration illuminate both the promises and challenges associated with the integration of AI and ML in cybersecurity. Ethical considerations, vulnerabilities to adversarial attacks, and the exigency for quantum-resistant cryptography emerge as critical areas necessitating nuanced attention and exploration. This paper envisions a future where the fusion of human expertise with the capabilities of AI and ML results in the creation of resilient and adaptive cybersecurity ecosystems. The delineated research directions serve not only as a comprehensive roadmap for ongoing innovation but also as a foundational guide to effectively integrate AI and ML in safeguarding our digital realm against the ever-evolving landscape of cyber threats.

Advancements in predictive maintenance in the oil and gas industry: A review of AI and data science applications

This study provides a comprehensive review of the advancements in predictive maintenance within the oil and gas industry, focusing on the integration and impact of Artificial Intelligence (AI) and Data Science. The primary objective was to evaluate how AI and data science have transformed maintenance practices from traditional methods to more advanced, predictive approaches. The methodology involved a systematic literature review, utilizing databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Web of Science. The search strategy was centered around keywords related to AI, data science, and predictive maintenance in the oil and gas sector, with a focus on literature published from 2010 onwards. The findings reveal that AI and data science significantly enhance predictive maintenance strategies. AI algorithms and data analytics have enabled more accurate predictions of equipment failures and optimized maintenance scheduling, leading to reduced downtime and operational costs. The study also identifies challenges, including the complexity of data management and the need for high-quality, real-time data. Opportunities for future advancements lie in developing more robust AI models capable of adapting to the industry's dynamic environment. The study recommends that industry stakeholders invest in workforce training for AI-based systems and that policymakers develop frameworks supporting ethical AI use. Future research directions include exploring the integration of AI with other emerging technologies and developing sustainable maintenance practices. The study concludes that AI's continuous evolution will play a crucial role in shaping the future of maintenance strategies in the oil and gas industry.

Staying Ahead of Threats: A Review of AI and Cyber Security in Power Generation and Distribution

The integration of artificial intelligence (AI) and the Internet of Things (IoT) in the power generation and distribution industry presents opportunities and challenges, particularly in the area of cybersecurity. Previous studies have explored the potential of AI to enhance cybersecurity in power systems, but limitations in terms of sample size and scope have hindered a comprehensive understanding of the current state of the field. To address this gap, this paper presents a systematic literature review of 30 papers that analyzes and categorizes relevant research based on their focus on threats, solutions, and future trends. The results indicate that 30 articles provide evidence supporting the use of AI and machine learning techniques to significantly enhance cybersecurity in the power sector. However, the study also highlights the need for continuous monitoring, threat intelligence, and risk management to stay ahead of evolving threats. Notably, this paper provides novel insights into the use of cybersecurity measures, blockchain technology, and awareness of the impact of AI in the power sector, with 90% of organizations using cybersecurity measures, 50% employing blockchain technology, 20% experiencing a cyberattack, and 60% being aware of the impact of AI. The study's limitations include a lack of detailed information on the organizations studied, such as their size and location, and the absence of a standardized approach to data collection across the selected papers. Nonetheless, this paper offers a valuable contribution to the field of AI and cybersecurity in the power industry by providing a comprehensive overview of the current state of research and identifying key areas for further investigation.

CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review.

Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings.

IDF21-0503 CHIldren Living with Diabetes See and Thrive with AI Review: CHILDSTAR

Background: Diabetic retinopathy (DR) is a common complication of diabetes, and if left un-treated, can lead to visual impairment and blindness. The prevalence of diabetes in children and young adults is rising rapidly worldwide. The role of artificial intelligence (AI) in the detection of DR appears promising, and the accuracy of AI systems to detect DR has been extensively studied in adult patients; however, there are very limited studies in children. Orbis International’s Cybersight AI provides automated recommendations for DR staging from fundus photographs based on Deep Learning algorithms, which were trained using data from adult patients (18+ years).

A Review of AI (Artificial Intelligence) Tools and Customer Experience in Online Fashion Retail

Artificial Intelligence tools and processes have hugely impacted the ecommerce industry and the satisfaction of online customers. With technology largely pervading all facets of our lives, people want meaningful experiences. Artificial intelligence has the ability to deliver positive experiences for customers that helps build brand trust and customer satisfaction. Whether you are using your smartphone, laptop or voice assistants such as Alexa or Siri, service on the internet is gaining new ground. This paper does a literature review of the various technological advances that optimize the customer experience to evoke e-satisfaction, i.e. satisfaction while shopping online. E-satisfaction as a construct will be reviewed and its impact on customer purchase intention. This review will provide businesses and other researchers a frame of reference to conduct empirical studies in the area of AI and technology enabled retail.

The Opportunity and Challenge of the Development of Artificial Intelligence to Human Labor Liberation

With the development and application of artificial intelligence (AI) technology, it has promoted the upgrading and development of the industry. However, AI also brings intangible impact to the actual survival of human beings. Stephen·Hawking, the renowned physicist, has repeatedly warned, making it all the more urgent to reflect on the negative effects of artificial intelligence. Therefore, the review of AI should consider from the perspective of historical development, scientifically explain its changes in the way of human labor and social life, as well as the great significance of such changes to the liberation of human labor.