Artificial Intelligence Strategies Research Articles

AI-Driven Cybersecurity: Balancing Advancements and Safeguards

As Artificial Intelligence (AI) continues its rapid evolution, its profound influence on cybersecurity becomes increasingly evident. This study delves into the pivotal role of AI in fortifying cybersecurity measures, emphasizing its capacity for enhanced threat detection, automated response mechanisms, and the development of resilient security frameworks. However, alongside its promise, recognition of AI's susceptibility to exploitation in sophisticated cyber-attacks exists, underscoring the imperative for continual advancements in AI-driven security solutions. This research offers a nuanced perspective on AI's impact on cybersecurity, advocating for the proactive integration of AI strategies, sustained research efforts, and formulating ethical guidelines. Adopting supervised machine learning (ML) algorithms like decision trees, support vector machines, and neural networks aims to harness AI's potential to bolster cybersecurity while concurrently addressing associated risks, paving the way for a secure digital landscape. Regarding accuracy, the neural network outperforms other models by 98%.

The Classroom Reimagined: Setting the Tone, Artificial Intelligence and Icebreaker Strategies

There’s no denying it - in academia, the first week of class is an imperative time for professors and students alike. In academic circles, the topic of student buy-in to encourage active learning often dominates discourse. Through careful planning and course delivery, educators set the tone during the first week of class and create lasting impressions which can enrich the educational experience for students. This time also affords students the opportunity to settle in, become acquainted with their professor and classmates, and begin to plan for the rest of the semester. This article explores the topic of setting the tone for course delivery and the use of artificial intelligence (AI) in the classroom. Pedagogical recommendations for the first week of class and the use of AI are offered. AI-enhanced icebreakers are also discussed as instructional strategies for course planning and delivery.

Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development.

Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, an integrated information system is developed for the pathological-level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface-enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups are successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, it is identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance.

Integration of AI into the Distance Learning Environment: Enhancing Soft Skills

Soft skills have become increasingly essential for success in the modern world, especially in the labor market, where employers value employees’ social and communication skills. Online education, which is an integral part of the educational process in Ukraine, is adjusting to the development of students’ soft skills. Integrating artificial intelligence tools into English language learning is becoming a new direction in soft skills development. This approach opens up new teaching strategies that make learning more effective, engaging, and innovative. While learning English, students develop communication, creativity, and critical thinking skills, which contribute to their educational process and prepare the foundation for employment. The study aims to 1) evaluate the impact of artificial intelligence on the development of students’ soft skills in online learning; 2) identify the most essential soft skills for their effective learning and future employment based on a student survey; 3) develop criteria for an online course using artificial intelligence and outline strategies for integrating the ChatGPT tool into distance learning English classes. To achieve the objectives of our study, we developed and processed a questionnaire, collected quantitative data, and analyzed and interpreted qualitative data; the study sample included 304 students. The questionnaire results showed a generally positive attitude of students towards using artificial intelligence in English for Specific Purposes courses. They opened up prospects for introducing an online course in the English for Specific Purposes program and further research, including an experiment with the introduction of this online course.

Imagining and governing artificial intelligence: the ordoliberal way—an analysis of the national strategy ‘AI made in Germany’

AbstractNational Artificial Intelligence (AI) strategies articulate imaginaries of the integration of AI into society and envision the governing of AI research, development and applications accordingly. To integrate these central aspects of national AI strategies under one coherent perspective, this paper presented an analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality. The first part of the paper analyses how the guiding vision of a human-centric AI not only adheres to ethical and legal principles consistent with Germany’s liberal democratic constitutional system but also addresses the risks and promises inherent to the ordoliberal problematization of freedom. Second, it is scrutinized how the strategy cultivates the fear of not achieving technological sovereignty in the AI sector. Thereby, it frames the global AI race as a race of competing (national) approaches to governing AI and articulates an ordoliberal approach to governing AI (the ‘third way’), according to which government has to operate between the twin dangers of governing too much and not governing enough. Third, the paper analyses how this ordoliberal proportionality of governing structures Germany’s Science Technology & Innovation Policy. It is shown that the corresponding risk-based approach of regulating AI constitutes a security apparatus as it produces an assessment of fears: weighting the fear of the failure to innovate with the fear of the ramifications of innovation. Finally, two lines of critical engagement based on this analysis are conducted.

The Application of Artificial Intelligence (Ai) and Internet of Things (Iot) Middleware in Agriculture

Artificial intelligence (AI) devices like master frameworks, regular language handling, discourse acknowledgment, and machine vision have modified both the amount and nature of work in the rural area because of the developing worldwide populace, the rising interest for food, as well as changes in climate and the availability of water. Specialists and researchers are presently attempting to involve new IoT advances in savvy cultivating to help ranchers in making better seeds, crop assurance, and manures utilizing AI technology. Both the nation's overall economy and the profitability of ranchers will be benefited from this. Farming robots, prescient investigation, and soil and yield observing  are the three main regions where AI is beginning to appear. In this sense, ranchers are utilizing sensors and soil test all the more often to gather information that the executives frameworks might use for more examination. Through an outline of AI applications in the farming business, this paper makes a commitment to the area. It starts with a prologue to AI and an investigation of all AI strategies utilized in the horticultural area, including AI, the Internet of Things using Universal Middleware, master frameworks, picture handling, and PC vision. This study incorporates a complete examination of the literature to research how artificial intelligence (AI) and the Internet of Things (IoT) are utilized in farming.

Artificial Intelligence in the French Law of 2024

The use of artificial intelligence in France is growing and intensifying in many areas, particularly in the field of justice. French President Macron has made it one of his government’s priorities to build on these assets and make France a world leader in AI. In parallel, the French government has deployed some efforts towards anticipating the regulatory challenges related to AI, the “National Strategy for Artificial Intelligence” launched as part of «France 2030» . As an illustration of the developments in artificial intelligence and its specific regulation, the French parliament passed a law to ensure the proper conduct of the 2024 Olympic and Paralympic Games (Law N° 2023-380 of 19.05.2023). The law permits the use of the experimental “augmented video-protection” technology, which uses cameras equipped with AI systems to detect and report specific events in real time. French regulations begin already now in the area of justice and must continue in the fields of AI liability and intellectual property. AI is a source of fears, particularly for the respect of human rights, and requires a very elaborate legal and ethical environment that is flexible enough to avoid slowing down the development of AI. The AI Liability EU Directive complements the Artificial Intelligence Act by introducing a new liability regime that ensures legal certainty, enhances consumer trust in AI, and assists consumers’ liability claims for damage caused by AI-enabled products and services. But the new European AI Act does not resolve all issues that therefore need to be addressed nationally. 

Democracy out-of-the-box: analysis of compliance with constitutional principles in tax policies that use Artificial Intelligence

The Democratic State of Law emerged with the objective of improving everyone’s life, restricting the power of tyrants, who, among other arbitrary acts, unfairly taxed the people. While taxes remain crucial for the State’s upkeep, modern rules prevent individuals from enduring excessive burdens. Although new technologies leveraging Artificial Intelligence (AI) hold the promise of enhancing lives, the extent of this improvement raises questions. This study delves into the relationship between individuals and the State, specifically exploring the use of AI in tax-related scenarios. Conducting a comprehensive three-stage investigation, the first stage involved surveying the reasons behind State-imposed limitations on taxation. The second stage identified criteria from computer literature addressing potential AI challenges and strategies for achieving explainability. Additionally, a discussion emphasized the importance of technological models aligning with principles that underpin our society. Then, to understand how actions have been carried out in Public Administration, a Systematic Mapping Study (SMS) was carried out, analyzing works from the ACM Digital Library, SBC OpenLib (SOL) and the ENAP (National School of Public Administration)’s repository (a Gray Literature repository). On the ENAP website, works relating to the Creativity and Innovation Award from the Federal Revenue of Brazil were specifically consulted. In the end, 10 works that met the inclusion criteria were selected. It was concluded that none of them has an AI explainable model, considering the interpretability criteria of models (Rules and logic, Data visualization, and Model documentation). In light of this, it is recommended that studies addressing the use of AI in Public Administration incorporate a dedicated section discussing the explainability of models.

[The Future of Nursing Education: Necessary Elements and Implementation Strategies for Learning Artificial Intelligence].

As populations age, average life expectancy increases and the complexity of diseases rises, leading to nursing care and healthcare systems facing severe challenges related to inadequate resources. Artificial intelligence (AI), including elements such as investigation, integration, learning, prediction, and decision-making, holds significant potential for application in clinical care not only to enhance care quality but also to help guide the future direction of healthcare. AI applications are already being increasingly utilized to improve the quality of clinical care and to streamline workflows. However, because nursing education has lagged behind in terms of adopting AI, greater attention must be given to training up nursing students with AI-related knowledge and application skills. AI technologies should be integrated into nursing curricula and clinical internships to adapt to the rapidly changing high-tech healthcare environment, enabling the more-effective use of AI technology in providing high-quality and safe nursing care.

Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations

Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.

Accelerating the Development of Thin Film Photovoltaic Technologies: An Artificial Intelligence Assisted Methodology Using Spectroscopic and Optoelectronic Techniques.

Thin film photovoltaic (TFPV) materials and devices present a high complexity with multiscale, multilayer, and multielement structures and with complex fabrication procedures. To deal with this complexity, the evaluation of their physicochemical properties is critical for generating a model that proposes strategies for their development and optimization. However, this process is time-consuming and requires high expertise. In this context, the adoption of combinatorial analysis (CA) and artificial intelligence (AI) strategies represents a powerful asset for accelerating the development of these complex materials and devices. This work introduces a methodology to facilitate the adoption of AI and CA for the development of TFPV technologies. The methodology covers all the necessary steps from the synthesis of samples for CA to data acquisition, AI-assisted data analysis, and the extraction of relevant information for research acceleration. Each step provides details on the necessary concepts, requirements, and procedures and are illustrated with examples from the literature. Then, the application of the methodology to a complex set of samples from a TFPV production line highlights its ability to rapidly glean significant insights even in intricate scenarios. The proposed methodology can be applied to other types of materials and devices beyond PV and using different characterization techniques.

IMPACT OF IMPLEMENTING AI IN MEDICAL FIELD

Introduction: AI is a field of great scope and latest leading fame and coming to it in medical field it has been very useful and impactful. Objectives: we can say that the objectives of bringing in artificial intelligence in healthcare applications revolve around maximising AI's capabilities to enhance working capabilities, efficiency, and personalized care delivery to patient’s, ultimately leading to improved patient outcomes and healthcare operation. Review of Literature: A mixer of different papers regarding patient’s, medical field and related things Results: AI in the medical field is critical to the advancement of medical robotic surgery, IT operations, health tracking, and diagnostics using automation, predictive analytics, and data-driven insights to improve efficiency, accuracy, and patient care in a variety of healthcare and technology areas. Discussion: By the study and research we can observe that, artificial intelligence is transforming the healthcare industry in numerous areas giving it various wings. Implementation: Healthcare companies may boost operational effectiveness and implement AI strategies, improve patient outcomes, and provide more individualized and efficient care by utilizing AI technologies. Conclusion: The use of AI in medical field is useful as it acts as a good assistance and support, artificial intelligence (AI) allows healthcare businesses to give the best scope and power. Keywords: Artificial intelligence, predictive analytics, Patient , Clinical decision-making, Management, robotic surgery, healthcare, efficient care.

An artificial intelligence strategy for the deployment of future microservice-based applications in 6G networks

An artificial intelligence strategy for the deployment of future microservice-based applications in 6G networks

Construction by artificial intelligence and immunovalidation of hypoallergenic mite allergen Der f 36 vaccine.

The house dust mite (HDM) is widely recognized as the most prevalent allergen in allergic diseases. Allergen-specific immunotherapy (AIT) has been successfully implemented in clinical treatment for HDM. Hypoallergenic B-cell epitope-based vaccine designed by artificial intelligence (AI) represents a significant progression of recombinant hypoallergenic allergen derivatives. The three-dimensional protein structure of Der f 36 was constructed using Alphafold2. AI-based tools were employed to predict B-cell epitopes, which were subsequently verified through IgE-reaction testing. Hypoallergenic Der f 36 was then synthesized, expressed, and purified. The reduced allergenicity was assessed by enzyme-linked immunosorbent assay (ELISA), immunoblotting, and basophil activation test. T-cell response to hypoallergenic Der f 36 and Der f 36 was evaluated based on cytokine expression in the peripheral blood mononuclear cells (PBMCs) of patients. The immunogenicity was evaluated and compared through rabbit immunization with hypoallergenic Der f 36 and Der f 36, respectively. The inhibitory effect of the blocking IgG antibody on the specific IgE-binding activity and basophil activation of Der f 36 allergen was also examined. The final selected non-allergic B-cell epitopes were 25-48, 57-67, 107-112, 142-151, and 176-184. Hypoallergenic Der f 36 showed significant reduction in IgE-binding activity. The competitive inhibition of IgE-binding to Der f 36 was investigated using the hypoallergenic Der f 36, and only 20% inhibition could be achieved, which is greatly reduced when compared with inhibition by Der f 36 (98%). The hypoallergenic Der f 36 exhibited a low basophil-stimulating ratio similar to that of the negative control, and it could induce an increasing level of IFN-γ but not Th2 cytokines IL-5 and IL-13 in PBMCs. The vaccine-specific rabbit blocking IgG antibodies could inhibit the patients' IgE binding and basophil stimulation activity of Derf 36. This study represents the first application of an AI strategy to facilitate the development of a B-cell epitope-based hypoallergenic Der f 36 vaccine, which may become a promising immunotherapy for HDM-allergic patients due to its reduced allergenicity and its high immunogenicity in inducing blocking of IgG.

Women’s attitudes and perspectives on the use of artificial intelligence in the assessment of screening mammograms

PurposeTo investigate attitudes and perspectives on the use of artificial intelligence (AI) in the assessment of screening mammograms among women invited to BreastScreen Norway. MethodAn anonymous survey was sent to all women invited to BreastScreen Norway during the study period, October 10, 2022, to December 25, 2022 (n = 84,543). Questions were answered on a 10-point Likert scale and as multiple-choice, addressing knowledge of AI, willingness to participate in AI studies, information needs, confidence in AI results and AI assisted reading strategies, and thoughts on concerns and benefits of AI in mammography screening. Analyses were performed using χ2 and logistic regression tests. ResultsGeneral knowledge of AI was reported as extensive by 11.0% of the 8,355 respondents. Respondents were willing to participate in studies using AI either for decision support (64.0%) or triaging (54.9%). Being informed about use of AI-assisted image assessment was considered important, and a reading strategy of AI in combination with one radiologist preferred. Having extensive knowledge of AI was associated with willingness to participate in AI studies (decision support; odds ratio [OR]: 5.1, 95% confidence interval [CI]: 4.1–6.4, and triaging; OR: 3.4, 95% CI: 2.8–4.0) and trust in AI’s independent assessment (OR: 6.8, 95% CI: 5.7, 8.3). ConclusionsWomen invited to BreastScreen Norway had a positive attitude towards the use of AI in image assessment, given that human readers are still involved. Targeted information and increased public knowledge of AI could help achieve high participation in AI studies and successful implementation of AI in mammography screening.

Enhanced Artificial Intelligence Strategies in Renal Oncology: Iterative Optimization and Comparative Analysis of GPT 3.5 Versus 4.0.

The rise of artificial intelligence (AI) in medicine has revealed the potential of ChatGPT as a pivotal tool in medical diagnosis and treatment. This study assesses the efficacy of ChatGPT versions 3.5 and 4.0 in addressing renal cell carcinoma (RCC) clinical inquiries. Notably, fine-tuning and iterative optimization of the model corrected ChatGPT's limitations in this area. In our study, 80 RCC-related clinical questions from urology experts were posed three times to both ChatGPT 3.5 and ChatGPT 4.0, seeking binary (yes/no) responses. We then statistically analyzed the answers. Finally, we fine-tuned the GPT-3.5 Turbo model using these questions, and assessed its training outcomes. We found that the average accuracy rates of answers provided by ChatGPT versions 3.5 and 4.0 were 67.08% and 77.50%, respectively. ChatGPT 4.0 outperformed ChatGPT 3.5, with a higher accuracy rate in responses (p<0.05). By counting the number of correct responses to the 80 questions, we then found that although ChatGPT 4.0 performed better (p<0.05), both versions were subject to instability in answering. Finally, by fine-tuning the GPT-3.5 Turbo model, we found that the correct rate of responses to these questions could be stabilized at 93.75%. Iterative optimization of the model can result in 100% response accuracy. We compared ChatGPT versions 3.5 and 4.0 in addressing clinical RCC questions, identifying their limitations. By applying the GPT-3.5Turbo fine-tuned model iterative training method, we enhanced AI strategies in renal oncology. This approach is set to enhance ChatGPT's database and clinical guidance capabilities, optimizing AI in this field.

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

EU AI sovereignty: for whom, to what end, and to whose benefit?

ABSTRACT EU policy emphasises the need for digital sovereignty, also in artificial intelligence (AI). But because such ‘AI sovereignty’ could be used for diverse and even conflicting goals, it obscures the tensions that actual EU AI strategy entails. Conceptually, this article proposes three central trade-offs: first, does AI sovereignty pit the EU against other major AI powers, or rather citizens against large tech companies? Second, is AI sovereignty meant to boost the EU’s position in a putative AI race, or is it a means to defy this competitiveness-logic, instead? And third, is EU AI sovereignty primarily meant to benefit European citizens, or does it embrace a global responsibility? The empirical analysis then maps the EU Commission’s AI strategy since 2018 and the EU AI Act negotiations onto these three dimensions. It reveals that EU AI policy prioritises jurisdictional independence over citizens sovereignty, that it embraces a global AI race logic, and that it largely neglects its impact on people beyond the EU. This orientation of EU AI sovereignty resonates with and feeds into rising geo-economic tensions. But it would underestimate the importance of public policy to assume that it flowed naturally from technological transformations, rather than from political choices.

Future directions of artificial intelligence integration: Managing strategies and opportunities

Embracing Artificial Intelligence (AI) is becoming more common in a variety of areas, including healthcare, banking, and transportation, and it is based on substantial data analysis. However, utilizing data for AI raises a number of obstacles. This extensive article examines the challenges connected with using data for AI, including data quality, volume, privacy and security, bias and fairness, interpretability and ethical considerations, and the required technical knowledge. The investigation delves into each obstacle, providing insightful solutions for businesses and organizations to properly handle these complexities. Organizations may effectively harness AI’s capabilities to make educated decisions by understanding and proactively tackling these difficulties, obtaining a competitive edge in the digital era. This review study, which provides a thorough examination of numerous solutions developed over the last decade to address data difficulties for AI, is expected to be a helpful resource for the scientific research community. It not only provides insights into current difficulties, but it also serves as a platform for creating novel ideas to alter our approaches to data strategies for AI.

Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment.

This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up. For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis. Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI. This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future. These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics. • This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. • MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. • The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.