Artificial Intelligence Research Research Articles

Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security

AbstractThis survey paper explores the transformative role of Artificial Intelligence (AI) in information security. Traditional methods, especially rule-based approaches, faced significant challenges in protecting sensitive data from ever-changing cyber threats, particularly with the rapid increase in data volume. This study thoroughly evaluates AI’s application in information security, discussing its strengths and weaknesses. It provides a detailed review of AI’s impact on information security, examining various AI algorithms used in this field, such as supervised, unsupervised, and reinforcement learning, and highlighting their respective strengths and limitations. The study identifies key areas for future AI research in information security, focusing on improving algorithms, strengthening information security, addressing ethical issues, and exploring safety and security-related concerns. It emphasizes significant security risks, including vulnerability to adversarial attacks, and aims to enhance the robustness and reliability of AI systems in protecting sensitive information by proposing solutions for potential threats. The findings aim to benefit cybersecurity professionals and researchers by offering insights into the intricate relationship between AI, information security, and emerging technologies.

A Review of Recent Hardware and Software Advances in GPU-Accelerated Edge-Computing Single-Board Computers (SBCs) for Computer Vision.

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain.

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The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the financial sector has brought about a profound transformation in decision-making processes, risk management, and predictive analytics. This comprehensive study aims to systematically identify and analyze the foundational theories, emerging themes, and research clusters within the extensive body of AI and ML finance literature through an in-depth bibliometric analysis. By meticulously examining a vast array of publications spanning over two decades, the study uncovers the intricate evolution of AI and ML applications in finance, mapping out key areas of research and providing valuable insights into future research directions. The findings reveal a significant and accelerating growth in the application of AI and ML across various financial domains, notably in fraud detection, portfolio management, and algorithmic trading, demonstrating the substantial impact and transformative potential of these technologies. This study not only charts the current landscape of AI and ML research in finance but also identifies critical gaps and opportunities for future exploration, underscoring the ongoing evolution and maturation of this dynamic field.

The Exploration and Research of AI+ Intelligent Machine Vision in Practice Teaching

The rapid development and gradual perfection of artificial intelligence (AI) and intelligent machine vision technology have promoted the expansion of their application fields. And at this stage, AI+ intelligent machine vision has been widely used in practical teaching. Moreover, because artificial intelligence (AI) and intelligent machine vision technology involve many knowledge subjects, it is necessary to explore and study them effectively in order to give full play to their effectiveness in practical teaching.

Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.

Drones (unoccupied aircraft systems) have become effective tools for wildlife monitoring and conservation. Automated animal detection and classification using artificial intelligence (AI) can substantially reduce logistical and financial costs and improve drone surveys. However, the lack of annotated animal imagery for training AI is a critical bottleneck in achieving accurate performance of AI algorithms compared to other fields. To bridge this gap for drone imagery and help advance and standardize automated animal classification, we have created the Aerial Wildlife Image Repository (AWIR), which is a dynamic, interactive database with annotated images captured from drone platforms using visible and thermal cameras. The AWIR provides the first open-access repository for users to upload, annotate, and curate images of animals acquired from drones. The AWIR also provides annotated imagery and benchmark datasets that users can download to train AI algorithms to automatically detect and classify animals, and compare algorithm performance. The AWIR contains 6587 animal objects in 1325 visible and thermal drone images of predominantly large birds and mammals of 13 species in open areas of North America. As contributors increase the taxonomic and geographic diversity of available images, the AWIR will open future avenues for AI research to improve animal surveys using drones for conservation applications. Database URL: https://projectportal.gri.msstate.edu/awir/.

A Review of Digital Pathology and AI Applications for Urine Cytology

The aims of this article are to review the historical perspective, current utilization, challenges, and ongoing research in digital pathology and artificial intelligence (AI) applications for urine cytology. A combination of expert opinions as well as current published literature were used to find all relevant information. The expert opinions are mostly based on the author’s research and professional experience as a practicing and board-certified cytopathologist and clinical informaticist. The Curation of literature is based on searchable publications available through Google Scholar and PubMed. The findings revealed relevant historical work as well as how the diversity of cytopathology practice can create significant challenges moving forward with digital pathology and AI. Twenty-six relevant peer reviewed publications collaborated historical and ongoing research efforts in both academic and commercial arenas. The ongoing research and commercial efforts will advance the applications of digital pathology and AI for urine cytology despite many challenges

Segmentation and Classification of Medical Images Using Artificial Intelligence: A Review

In the realm of medical imaging, segmenting and classifying medical images is essential for helping medical professionals diagnose and treat a variety of medical disorders. This review discusses artificial intelligence (AI) techniques. AI systems have shown impressive accuracy and efficiency in identifying and quantifying elements in medicalimages, such as MRI, CT, and X-rays, by utilizing deep learning techniques, in particularconvolutional neural network (CNNs). We describe CNN design and training for medical image segmentation and classification, emphasizing the usefulness of CNNs in identifying and defining diseased regions and anatomical features. Even with obstacles like data privacy, requiring sizable annotated datasets, and requiring model interpretability, further research and development in AL-driven medical images analysis has promise for improving clinical decision-making and diagnostic accuracy. Future research in this area should concentrate on improving the generalizability and resilience of AI models by using methods like data augmentation, transfer learning, and the creation of more complex network topologies. Furtheringthe area also requires guaranteeing ethical concerns, enhancing data-sharing mechanisms, and encouraging cooperation between medical practitionersand AI researchers.

Generative Artificial Intelligence: Enhancing Patient Education in Cardiovascular Imaging.

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.

Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence.

This systematic literature review aims to study the role and impact of artificial intelligence (AI) in transforming Ear, Nose, and Throat (ENT) healthcare. It aims to compare and analyse literature that applied AI algorithms for ENT disease prediction and detection based on their effectiveness, methods, dataset, and performance. We have also discussed ENT specialists' challenges and AI's role in solving them. This review also discusses the challenges faced by AI researchers. This systematic review was completed using PRISMA guidelines. Data was extracted from several reputable digital databases, including PubMed, Medline, SpringerLink, Elsevier, Google Scholar, ScienceDirect, and IEEExplore. The search criteria included studies recently published between 2018 and 2024 related to the application of AI for ENT healthcare. After removing duplicate studies and quality assessments, we reviewed eligible articles and responded to the research questions. This review aims to provide a comprehensive overview of the current state of AI applications in ENT healthcare. Among the 3257 unique studies, 27 were selected as primary studies. About 62.5% of the included studies were effective in providing disease predictions. We found that Pretrained DL models are more in application than CNN algorithms when employed for ENT disease predictions. The accuracy of models ranged between 75 and 97%. We also observed the effectiveness of conversational AI models such as ChatGPT in the ENT discipline. The research in AI for ENT is advancing rapidly. Most of the models have achieved accuracy above 90%. However, the lack of good-quality data and data variability limits the overall ability of AI models to perform better for ENT disease prediction. Further research needs to be conducted while considering factors such as external validation and the issue of class imbalance.

Groove pancreatitis: From enigma to future directions-A comprehensive review.

Groove pancreatitis (GP) is a rare and clinically distinct form of chronic pancreatitis affecting the pancreaticoduodenal groove comprising the head of the pancreas, duodenum, and the common bile duct. It is more prevalent in individuals in their 4-5th decade of life and disproportionately affects men compared with women. Excessive alcohol consumption, tobacco smoking, pancreatic ductal stones, pancreatic divisum, annular pancreas, ectopic pancreas, duodenal wall thickening, and peptic ulcers are significant risk factors implicated in the development of GP. The usual presenting symptoms include severe abdominal pain, nausea, vomiting, diarrhea, weight loss, and jaundice. Establishing a diagnosis of GP is often challenging due to significant clinical and radiological overlap with numerous benign and malignant conditions affecting the same anatomical location. This can lead to a delay in initiation of treatment leading to increasing morbidity, mortality, and complication rates. Promising research in artificial intelligence (AI) has garnered immense interest in recent years. Due to its widespread application in diagnostic imaging with a high degree of sensitivity and specificity, AI has the potential of becoming a vital tool in differentiating GP from pancreatic malignancies, thereby preventing a missed or delayed diagnosis. In this article, we provide a comprehensive review of GP, covering the etiology, pathogenesis, clinical presentation, radiological and endoscopic evaluation, management strategies, and future directions. This article also aims to increase awareness about this lesser known and often-misdiagnosed clinical entity amongst clinicians to ultimately improve patient outcomes.

Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.

Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data.Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance.The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.

The Role of Artificial Intelligence in Early Diagnosis and Molecular Classification of Head and Neck Skin Cancers: A Multidisciplinary Approach.

Cancer remains a significant global health concern, with increasing genetic and metabolic irregularities linked to its onset. Among various forms of cancer, skin cancer, including squamous cell carcinoma, basal cell carcinoma, and melanoma, is on the rise worldwide, often triggered by ultraviolet (UV) radiation. The propensity of skin cancer to metastasize highlights the importance of early detection for successful treatment. This narrative review explores the evolving role of artificial intelligence (AI) in diagnosing head and neck skin cancers from both radiological and pathological perspectives. In the past two decades, AI has made remarkable progress in skin cancer research, driven by advances in computational capabilities, digitalization of medical images, and radiomics data. AI has shown significant promise in image-based diagnosis across various medical domains. In dermatology, AI has played a pivotal role in refining diagnostic and treatment strategies, including genomic risk assessment. This technology offers substantial potential to aid primary clinicians in improving patient outcomes. Studies have demonstrated AI's effectiveness in identifying skin lesions, categorizing them, and assessing their malignancy, contributing to earlier interventions and better prognosis. The rising incidence and mortality rates of skin cancer, coupled with the high cost of treatment, emphasize the need for early diagnosis. Further research and integration of AI into clinical practice are warranted to maximize its benefits in skin cancer diagnosis and treatment.

Current Trends in Artificial Intelligence and Bovine Mastitis Research: A Bibliometric Review Approach.

Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area.

THE ARTIFICIAL INTELLIGENCE TECHNOLOGIES APPLICATION IN THE MILITARY SYSTEMS ANALYSIS

This article analyzes the use of artificial intelligence technologies in military systems. Artificial intelligence (AI) is one of the modern areas of information technology. The history of human-like mechanisms development dates back to ancient times and has gone through a complex evolutionary path from myths and legends about the first androids, mechanical chess players and other complex mechanisms with human behaviour to modern autonomous robots. The first research on AI in the modern sense began almost immediately since the appearance of the first computers. Since the late 1950s, AI researchers have been trying to create smart machines that mimic the brain. AI has become a key technology in various industries, including the military, in recent decades. Thanks to its characteristics, AI transforms traditional strategies and tactics into a military art. Various aspects of the artificial intelligence use are discussed, in particular autonomous weapons systems, forecasting and analytics, cybersecurity and strategic planning. The advantages and opportunities provided by the use of artificial intelligence by military formations are highlighted, as well as attention is drawn to the ethical and legal aspects of this issue. Artificial intelligence technologies open up new opportunities for the military in many aspects, from autonomous systems to analytics and strategic planning. However, it is also important to take into account the ethical and legal aspects of these technologies use, as well as the risks associated with their implementation. A balanced approach to the use of artificial intelligence will help ensure its effective application in military systems. The article focuses on the importance of a balanced approach to the artificial intelligence technologies use for military purposes, taking into account their capabilities and risks.

A dyeing clustering algorithm based on ant colony path-finding mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

Research integrity in the era of artificial intelligence: Challenges and responses.

The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.

Challenges for Non-Classical Reasoning in Contemporary AI Applications

In knowledge representation and reasoning, a key area in artificial intelligence research, non-classical logics play a prominent double role: firstly, non-classical logic languages allow for a precise and transparent encoding of domain specific knowledge. Secondly, as the logical languages are equipped with custom-tailored rules of logical inference, they make available a principled approach to derive new knowledge from previous information. In practice, the first aspect addresses data storage and retrieval, the second aspect the utilization of available information. This article briefly surveys contemporary challenges of NCL research in AI.

ПРИМЕНЕНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ИЗМЕРЕНИЯ УСПЕВАЕМОСТИ УЧАЩИХСЯ

This article explores the application of artificial intelligence (AI) for measuring student performance. It examines the potential of AI for automated assessment, educational data analysis, personalized feedback, and integration into educational platforms. Special attention is given to advanced AI systems, such as Claude by Anthropic, and their potential in personalizing learning, providing feedback, and processing big data. The ethical considerations and challenges of implementing AI in education are discussed, emphasizing the importance of a responsible and student-centered approach. The paper also delves into future directions for research and development in AI for education, including the advancement of adaptive learning, support for 21st-century skills, and integration with other cutting-edge technologies. Additionally, it addresses the implications of AI on educational equity and accessibility. The conclusion highlights AI's potential to transform the educational landscape and the importance of a balanced approach to its implementation, ensuring that it benefits all learners while addressing potential risks and ethical concerns.

Application of Artificial Intelligence in rehabilitation science: A scientometric investigation Utilizing Citespace

This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.

ChatGPT in Urology: Bridging Knowledge and Practice for Tomorrow's Healthcare, a Comprehensive Review.

Background: Among emerging AI technologies, Chat-Generative Pre-Trained Transformer (ChatGPT) emerges as a notable language model, uniquely developed through artificial intelligence research. Its proven versatility across various domains, from language translation to healthcare data processing, underscores its promise within medical documentation, diagnostics, research, and education. The current comprehensive review aimed to investigate the utility of ChatGPT in urology education and practice and to highlight its potential limitations. Methods: The authors conducted a comprehensive literature review of the use of ChatGPT and its applications in urology education, research, and practice. Through a systematic review of the literature, with a search strategy using databases, such as PubMed and Embase, we analyzed the advantages and limitations of using ChatGPT in urology and evaluated its potential impact. Results: A total of 78 records were eligible for inclusion. The benefits of ChatGPT were frequently cited across various contexts. In educational/academic benefits mentioned in 21 records (87.5%), ChatGPT showed the ability to assist urologists by offering precise information and responding to inquiries derived from patient data analysis, thereby supporting decision making; in 18 records (75%), advantages comprised personalized medicine, predictive capabilities for disease risks and outcomes, streamlining clinical workflows and improved diagnostics. Nevertheless, apprehensions were expressed regarding potential misinformation, underscoring the necessity for human supervision to guarantee patient safety and address ethical concerns. Conclusion: The potential applications of ChatGPT hold the capacity to bring about transformative changes in urology education, research, and practice. AI technology can serve as a useful tool to augment human intelligence; however, it is essential to use it in a responsible and ethical manner.