Artificial Intelligence Solutions Research Articles

Application of Artificial Intelligence in the Securities Market

Artificial Intelligence (AI) involves creating machines that replicate human cognitive functions, enabling them to think and act similarly to people. In the context of stock trading, AI refers to the use of technology designed to buy and sell shares with increased precision and efficiency compared to human capabilities. AI-driven systems are currently employed to predict stock market trends and analyze trading patterns among investors and brokers. Prominent financial institutions on Wall Street, such as Goldman Sachs and Morgan Stanley, are increasingly adopting specialized AI solutions, including data mining, natural language processing, and self-learning algorithms. These tools operate with greater speed than everyday applications. Additionally, AI aids wealth management firms in continuously monitoring market movements and adjusting portfolios to achieve target profits. While AI can significantly reduce workload and provide real-time recommendations, it does not entirely eliminate the need for human oversight. Keywords: artificial intelligence, securities, trading, forecasting.

Enhancing Cybersecurity in Uzbekistan: Leveraging Artificial Intelligence Solutions

Enhancing Cybersecurity in Uzbekistan: Leveraging Artificial Intelligence Solutions

An Evaluation Framework of Industry 4.0’s Impact on SDGs and its Application to AI Reporting

Digital technologies of Industry 4.0 (I4.0) are key drivers of sustainable development. However, addressing the impact of I4.0 on Sustainable Development Goals (SDGs) requires overcoming the challenge of lacking suitable assessment tools. Despite sustainability reporting serves to assess how companies face sustainability challenges, communicate efforts, and promote a sustainable corporate culture, a critical need arises for a comprehensive tool that clarifies the impact of I4.0 technologies on the economic, environmental, and social dimensions of sustainability. This paper proposes a conceptual framework that builds on existing literature regarding how I4.0 technologies contribute to SDGs and leverages the recognized linking between SDGs and Global Reporting Initiative (GRI) Standards to improve the reporting of SDG progress. Thus, the proposed framework identifies the GRI-based Key Performance Indicators necessary for a thorough understanding of how innovation stemming from I4.0, significantly impacts SDGs. Through a content analysis of sustainability reports of innovative companies in the electronic field, the study shows how the proposed framework can be applied in practice. Focusing on Artificial Intelligence (AI), the application case allows a comprehensive understanding of the transformative potential of AI solutions towards sustainable development.

AI in Inclusive Education: A Systematic Review of Opportunities and Challenges in the Indian Context

India’s educational landscape, characterised by its dynamism and diverse student populace, has witnessed a notable shift towards inclusive education principles, aiming to provide equitable learning opportunities for all. The burgeoning advancements in Artificial Intelligence (AI) offer a promising avenue to revolutionise traditional educational paradigms, making the intersection of AI and inclusive education pivotal, especially within India’s unique socio-cultural and economic context. This systematic review explores AI applications in inclusive education within India, highlighting key trends, successful implementations, and challenges. The findings reveal that AI can significantly enhance personalised learning, assistive technologies, and administrative efficiencies, contributing to more inclusive educational environments. However, challenges such as linguistic diversity, varying learning environments, and uneven technological infrastructure pose significant obstacles. The review underscores the need for context-specific AI solutions and robust policy frameworks to address these challenges, providing insights into how AI can be effectively harnessed to foster inclusive education in India, catering to the diverse needs of its student population.

Artificial intelligence in the diagnosis of cerebrovascular diseases using magnetic resonance imaging: A scoping review

AbstractThe field of radiology is currently undergoing revolutionary changes owing to the increasing application of artificial intelligence (AI). This scoping review identifies and summarizes the technical methods and clinical applications of AI applied to magnetic resonance imaging of cerebrovascular diseases (CVDs). Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews was adopted and articles listed in PubMed and Cochrane databases from January 1, 2018 to December 31, 2023, were assessed. In total, 67 articles met the eligibility criteria. We obtained a general overview of the field, including lesion types, sample sizes, data sources, and databases and found that nearly half of the studies used multisequence magnetic resonance as the input. Both classical machine learning and deep learning were widely used. The evaluation metrics varied according to the five main algorithm tasks of classification, detection, segmentation, estimation, and generation. Cross‐validation was primarily used with only one third of the included studies using external validation. We also illustrate the key questions of the CVD research studies and grade the clinical utility of their AI solutions. Although most attention is devoted to improving the performance of AI models, this scoping review provides information on the availability of algorithms, reliability of external validations, and consistency of evaluation metrics and may facilitate improved clinical applicability and acceptance.

AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education.

Artificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in telesurgery and telementoring. However, its potential to enhance data transmission efficiency and reliability in these fields remains unclear. While previous reviews have explored the general applications of telesurgery and telementoring in specific surgical contexts, this review uniquely focuses on AI models designed to optimise data transmission and mitigate delays. We conducted a comprehensive literature search on PubMed and IEEE Xplore for studies published in English between 2010 and 2023, focusing on AI-driven, surgery-related, telemedicine, and delay-related research. This review includes methodologies from journals, conferences, and symposiums. Our analysis identified a total of twelve AI studies that focus on optimising network resources, enhancing edge computing, and developing delay-robust predictive applications. Specifically, three studies addressed wireless network resource optimisation, two proposed low-latency control and transfer learning algorithms for edge computing, and seven developed delay-robust applications, five of which focused on motion data, with the remaining two addressing visual and haptic data. These advancements lay the foundation for a truly holistic and context-aware telesurgical experience, significantly transforming remote surgical practice and education. By mapping the current role of AI in addressing delay-related challenges, this review highlights the pressing need for collaborative research to drive the evolution of telesurgery and telementoring in modern robotic surgery.

Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management

This review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI’s applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implementation and future directions for integrating more advanced AI solutions into the winemaking industry will also be discussed, providing a comprehensive overview of AI’s potential to revolutionize traditional practices.

AI and Machine Learning Solutions for Real-Time Fault-Tolerant System Design in Critical Applications

This study presents an innovative framework for implementing real-time fault-tolerant systems using artificial intelligence (AI) and machine learning (ML) to enhance reliability and resilience in critical applications. Addressing the needs of sectors such as aerospace, healthcare, automotive, and industrial automation, the proposed system integrates fault detection, isolation, and recovery mechanisms into a multi-layered architecture. Through the use of deep learning for accurate anomaly detection and reinforcement learning for rapid fault isolation, the system achieves high fault tolerance with minimal latency. The framework leverages edge computing for real-time data processing, ensuring timely responses to faults without excessive computational demands. Results from multiple case studies demonstrate significant improvements in fault detection accuracy, isolation speed, and recovery rates, affirming the framework’s adaptability and effectiveness in high-stakes environments. These findings highlight the potential of AI-driven fault-tolerant systems to elevate operational safety and reliability standards across diverse critical industries.

AI-powered precision: breast carcinoma diagnosis through digital proliferation index (Ki-67) assessment in pathological anatomy

PurposeThe primary objective of the study is to enhance the accuracy and efficiency of assessing the proliferation index in cancer cells, specifically focusing on the role of Ki-67. The purpose is to address the limitations of traditional visual assessments conducted by pathologists by integrating AI technologies, particularly deep learning. By accurately computing the percentage of Ki-67-labeled cells, the research aims to streamline the diagnostic process, reduce subjectivity and contribute to the advancement of diagnostic precision in pathological anatomy.Design/methodology/approachThe research employs a methodological approach that integrates Ki-67, a non-histone nuclear protein, as a vital biomarker for assessing the proliferative status of cancer cells. Given the challenges associated with traditional visual assessments by pathologists, including inter- and intra-observer variability and time-consuming efforts, the study adopts a novel methodology leveraging artificial intelligence (AI) solutions. Deep learning is applied to precisely calculate the percentage of Ki-67-labeled cells. The process involves pathologists delineating the tumor area at x40 magnification, enabling the segmentation of various cell types (positive, negative and tumor-infiltrating lymphocytes). The subsequent percentage calculation enhances efficiency and minimizes subjectivity in the diagnostic process.FindingsDespite inherent errors, the research findings indicate that the model surpasses existing benchmarks, showcasing superior accuracy in terms of average error measurement. The comparison with diverse datasets and benchmarking against pathologists’ diagnoses contributes empirical evidence to support the effectiveness of the AI-based model in accurately computing the percentage of Ki-67-labeled cells. These findings signify a noteworthy advancement in diagnostic methodologies and reinforce the potential of AI technologies in improving the precision of cancer diagnostics within the realm of pathological anatomy.Originality/valueThe research contributes to the field by introducing an innovative approach that combines Ki-67 as a biomarker and AI technologies for improved diagnostic precision. The originality lies in the utilization of deep learning to calculate the percentage of labeled cells, mitigating the challenges associated with manual assessments. The validation of the model against diverse datasets and benchmarking against pathologists’ diagnoses demonstrates its superior accuracy, highlighting the value of integrating AI in pathological anatomy for enhanced diagnostic outcomes. The study represents a significant stride in original research, offering novel insights and methodologies in the pursuit of more precise cancer diagnostics.

Enhancing IoT Security in Vehicles: A Comprehensive Review of AI-Driven Solutions for Cyber-Threat Detection

Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people’s daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices “smart” and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security.

Accurate Modeling and Parameters Estimation of Photovoltaic Models: Analytical and Artificial Intelligence Solutions

Accurate Modeling and Parameters Estimation of Photovoltaic Models: Analytical and Artificial Intelligence Solutions

TinyML-Based Lightweight AI Healthcare Mobile Chatbot Deployment.

In healthcare applications, AI-driven innovations are set to revolutionise patient interactions and care, with the aim of improving patient satisfaction. Recent advancements in Artificial Intelligence have significantly affected nursing, assistive management, medical diagnoses, and other critical medical procedures. Many artificial intelligence (AI) solutions operate online, posing potential risks to patient data security. To address these security concerns and ensure swift operation, this study has developed a chatbot tailored for hospital environments, running on a local server, and utilising TinyML for processing patient data. Edge computing technology enables secure on-site data processing. The implementation includes patient identification using a Histogram of Gradient (HOG)-based classification, followed by basic patient care tasks, such as temperature measurement and demographic recording. The classification accuracy of patient detection was 95.8%. An autonomous temperature-sensing unit equipped with a medical-grade infrared temperature scanner detected and recorded patient temperature. Following the temperature assessment, the tinyML-powered chatbot engaged patients in a series of questions customised by doctors to train the model for diagnostic scenarios. Patients' responses, recorded as "yes" or "no", are stored and printed in their case sheet. The accuracy of the TinyML model is 95.3% and the on-device processing time is 217 ms. The implemented TinyML model uses only 8.8Kb RAM and 50.3Kb Flash memory, with a latency of only 4 ms. Each patient was assigned a unique ID, and their data were securely stored for further consultation and diagnosis via hospital management. This research demonstrates faster patient data recording and increased security compared to existing AI-based healthcare solutions, as all processes occur within the local host.

Technology Gap Analysis on the BIM-Enabled Design Process of Prefabricated Buildings: An Autoethnographic Study

This research explores the pivotal role of Building Information Modeling (BIM) technology in revolutionizing the design and construction of prefabricated buildings. It highlights the benefits of BIM-enabled design processes, including improved coordination and efficiency, while addressing challenges in integrating BIM with prefabricated elements. The study proposes artificial intelligence (AI) solutions to enhance architectural detailing, optimize workflows, and facilitate human–robot collaboration. Using autoethnographic methods, the research refines BIM-forward design through thematic analysis and technology gap (TG) assessments. It advocates for a comprehensive technology roadmap that emphasizes BIM libraries, external data sources, and the transition to a multi-dimensional BIM environment. This research contributes valuable insights to advance BIM practices in prefabricated building construction and offers a strategic framework to bridge existing TGs, enabling seamless integration of BIM and AI in future practices.

Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium.

The 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools. Each workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience. Key outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes. The symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

Enhancing supply chain security and transparency with ai and blockchain integration for future-proof solutions

The objective of this review paper is to examine the integration of Artificial Intelligence (AI) and blockchain technology to enhance supply chain security and transparency, providing future-proof solutions for global industries. As the digital landscape evolves, traditional supply chain management methods face increasing vulnerabilities and inefficiencies. This paper reviews existing literature to assess the current challenges and potential benefits of integrating AI and blockchain in supply chains. The review highlights how AI enhances predictive analytics, demand forecasting, and operational efficiency, while blockchain ensures immutable, decentralized ledgers for real-time tracking and verification of goods. The synergy between these technologies offers unprecedented levels of transparency, enabling stakeholders to detect and respond to anomalies swiftly. Case studies across various industries demonstrate significant improvements in reducing fraud, counterfeiting, and human errors, alongside enhanced compliance with regulatory standards. Conclusions drawn from this review underscore the transformative potential of AI and blockchain in creating resilient, transparent, and secure supply chains. The integration of these technologies not only addresses current vulnerabilities but also lays the foundation for adaptive, intelligent supply chain networks capable of withstanding future disruptions. This paper provides actionable insights and practical frameworks for businesses and policymakers aiming to implement AI and blockchain solutions, emphasizing the importance of strategic planning, cross-industry collaboration, and continuous innovation in achieving long-term supply chain sustainability and security.

Edge Artificial Intelligence for Electrical Anomaly Detection Based on Process-In-Memory Chip

Neural-networks (NNs) for the current feature analysis bring novel electrical safety functions in smart circuit breakers (CBs), especially for preventing the fire hazard from electric vehicle/bike battery charging. In this work, the edge artificial intelligence (AI) solutions for the electrical anomaly detection were designed and demonstrated based on the process-in-memory (PIM) AI chip. The ultra-low power and high-performance character of PIM AI chips enable the edge solution to embed in the limited space inside the circuit breaker and to detect improper battery charging at millisecond latency.

Choosing the best artificial intelligence tools for human resource management

The article presents a comprehensive review and comparative analysis of modern artificial intelligence (AI)-based platforms used in human resource (HR) management. Tools such as HireVue, Pymetrics, Eightfold.ai, Workday, and Textio are reviewed. Their functionality, usability, integration with existing systems, data security, cost, and user support are discussed. The article provides practical recommendations for selecting optimal AI solutions, taking into account the specific needs and priorities of different organizations. It also explores the prospective directions of AI development in HR and the ethical aspects of their implementation.

AI-Driven Security Solutions: Combating Cyber Threats with Machine Learning Models

AbstractThis paper describes how artificial intelligence (AI) and machine learning (ML) models can help improve cybersecurity by identifying and preventing different cyber threats. Standard security solutions are usually ineffective, especially with the rising incidences of phishing, malware, and DDoS attacks. AI models are more proactive with the help of better algorithms that first find their way to detect the patterns and threats and then take the necessary action within the least time possible. The purpose of this research is to assess the effectiveness of these models for guarding digital domains and it will also assess the integration of these tools in different fields including the financial sector, health care and e-business. The paper also discusses the methods used in these systems: It has branch or subcategories as supervised learning, unsupervised learning, deep learning, and neural networks. Using case studies and data analysis, the paper defines key advantages of AI solutions, such as faster and more accurate detection and solution scalability. However, there are limitations as well while using the algorithm. The approach is vulnerable to adversarial attacks, is associated with high false positive rates, and requires a large amount of data. Therefore, this work explores the extent and possibilities of how AI and ML are relevant to today’s world insecurity parlance and subsequent advancements that may be seen in future innovations within these fields concerning threat identification and mitigation.

Advancing AI Data Ethics in Nursing: Future Directions for Nursing Practice, Research, and Education.

The ethics of artificial intelligence (AI) are increasingly recognized due to concerns such as algorithmic bias, opacity, trust issues, data security, and fairness. Specifically, machine learning algorithms, central to AI technologies, are essential in striving for ethically sound systems that mimic human intelligence. These technologies rely heavily on data, which often remain obscured within complex systems and must be prioritized for ethical collection, processing, and usage. The significance of data ethics in achieving responsible AI was first highlighted in the broader context of health care and subsequently in nursing. This viewpoint explores the principles of data ethics, drawing on relevant frameworks and strategies identified through a formal literature review. These principles apply to real-world and synthetic data in AI and machine-learning contexts. Additionally, the data-centric AI paradigm is briefly examined, emphasizing its focus on data quality and the ethical development of AI solutions that integrate human-centered domain expertise. The ethical considerations specific to nursing are addressed, including 4 recommendations for future directions in nursing practice, research, and education and 2 hypothetical nurse-focused ethical case studies. The primary objectives are to position nurses to actively participate in AI and data ethics, thereby contributing to creating high-quality and relevant data for machine learning applications.

Etyczne projektowanie w dobie generatywnej sztucznej inteligencji

Implementation of AI solutions is unavoidable on the markets, where the combination of time deficits and the need for constant creation of new solutions is especially demanding, that is i.a. in creative industries. Like a lens, generative AI concentrates the majority of threats and possibilities, resulting from technological changes of the recent decade. Still, despite the constant media confusion, great number of viral content, guides, courses and codes, we have much work to do towards defining the rules and specifics of designers’ collaboration with AI, and the related ethical challenges. The AI development is ahead of legal regulations and stimulates changes, and the constant information overload does not encourage reflection on AI systems and their role in design. Becoming aware of the possibilities and limitations of generative AI, however, is the only way to its sustainable and ethical use. The first step will be to reflect on and understand the possibilities and changes AI carries in the area of creation, intellectual property, and narrowing the competency gap (the lack of competency in reference to a given human team). The second step will be the efficient identification of risks and indication of new areas of responsibility to designers, who would like to apply new technologies in their work. Keywords: design, ethics, artificial intelligence (AI), responsibility, designer’s role