Artificial Intelligence Research Research Articles

A Scoping Review of Artificial Intelligence Detection of Voice Pathology: Challenges and Opportunities.

Survey the current literature on artificial intelligence (AI) applications for detecting and classifying vocal pathology using voice recordings, and identify challenges and opportunities for advancing the field forward. PubMed, EMBASE, CINAHL, and Scopus databases. A comprehensive literature search was performed following the Preferred Reporting Items for Systematic Reviews and Meta-analysesExtension for Scoping Reviews guidelines. Peer-reviewed journal articles in the English language were included if they used an AI approach to detect or classify pathological voices using voice recordings from patients diagnosed with vocal pathologies. Eighty-two studies were included in the review between the years 2000 and 2023, with an increase in publication rate from one study per year in 2012 to 10 per year in 2022. Seventy-two studies (88%) were aimed at detecting the presence of voice pathology, 24 (29%) at classifying the type of voice pathology present, and 4 (5%) at assessing pathological voice using the Grade, Roughness, Breathiness, Asthenia, and Strainscale. Thirty-six databases were used to collect and analyze speech samples. Fourteen articles (17%) did not provide information about their AI model validation methodology. Zero studies moved beyond the preclinical and offline AI model development stages. Zero studies specified following a reporting guideline for AI research. There is rising interest in the potential of AI technology to aid the detection and classification of voice pathology. Three challenges-and areas of opportunities-for advancing this research are heterogeneity of databases, lack of clinical validation studies, and inconsistent reporting.

Against the singularity hypothesis

AbstractThe singularity hypothesis is a hypothesis about the future of artificial intelligence on which self-improving artificial agents will quickly become orders of magnitude more intelligent than the average human. Despite the ambitiousness of its claims, the singularity hypothesis has been defended at length by leading philosophers and artificial intelligence researchers. In this paper, I argue that the singularity hypothesis rests on undersupported growth assumptions. I show how leading philosophical defenses of the singularity hypothesis fail to overcome the case for skepticism. I conclude by drawing out philosophical and policy implications of this discussion.

AI EOS using Wolframe Alpha

Artificial intelligence is crucial to day-to-day life. science of computers defines AI research as the study of intelligent agents. Today, most people, intentionally or not, have turned to some form of computerized information processing technology. Artificial Intelligence (AI) is already changing our lifestyle. A device that senses its surroundings and performs actions that maximize the likelihood of achieving a goal. The input to a database can be a choose to of users and articles, and the output is a ruthless recommendation. Input can be verbally or textually submitted by the user within the system. This paper presents a new approach to intelligent search. Overall, there are many people around the world who use assistants. This paper describes the provocation of applying virtual assistant technology. The paper also introduces the application of virtual assistants that can help open opportunities for humanity in various fields. Voice control is an important growing feature that will change people's lives. Voice assistants are available for laptops, desktops, and mobile phones. Assistant is now available on all electronic devices. A voice assistant is a software agent that can interpret human speech and respond in machine language

A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.

Level IV.

Bored to death: Artificial Intelligence research reveals the role of boredom in suicide behavior

BackgroundRecent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited.ObjectiveThis study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors.MethodsThe primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom.ResultsAn almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed.ConclusionIntegrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive ‘ingredient’ that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians’ attention to this burdening, and sometimes existential experience.

The influence of artificial intelligence in higher education based on four thematic axes: a bibliometric study

The article presents a structured bibliometric study examining the impact of artificial intelligence (AI) in higher education across four thematic axes: AI in higher education, AI in education review, AI in the teaching-learning process, and AI tools applied to higher education. Research productivity and impact indicators are analyzed using data from major databases like Scopus, Web of Science, and ScienceDirect. Results reveal a significant increase in AI-related research output, particularly in machine learning, data mining, and learning analytics. The study highlights China and the United States as leading contributors to AI research in higher education. The findings highlight AI's evolving role in transforming higher education and the need for multidisciplinary research approaches to address emerging challenges and opportunities. However, limitations include the reliance on quantitative measures, the narrow temporal scope, and the limited focus on high-production countries. Future research should incorporate qualitative methods to explore practical applications and social impacts more comprehensively, consider a broader range of geographic contexts, and discuss ethical considerations around integrating AI into higher education.

Application of deep learning to pressure injury staging.

Accurate assessment of pressure injuries (PIs) is necessary for a good outcome. Junior and non-specialist nurses have less experience with PIs and lack clinical practice, and so have difficulty staging them accurately. In this work, a deep learning-based system for PI staging and tissue classification is proposed to help improve its accuracy and efficiency in clinical practice, and save healthcare costs. A total of 1610 cases of PI and their corresponding photographs were collected from clinical practice, and each sample was accurately staged and the tissues labelled by experts for training a Mask Region-based Convolutional Neural Network (Mask R-CNN, Facebook Artificial Intelligence Research, Meta, US) object detection and instance segmentation network. A recognition system was set up to automatically stage and classify the tissues of the remotely uploaded PI photographs. On a test set of 100 samples, the average precision of this model for stage recognition reached 0.603, which exceeded that of the medical personnel involved in the comparative evaluation, including an enterostomal therapist. In this study, the deep learning-based PI staging system achieved the evaluation performance of a nurse with professional training in wound care. This low-cost system could help overcome the difficulty of identifying PIs by junior and non-specialist nurses, and provide valuable auxiliary clinical information.

ВІДПОВІДАЛЬНЕ ТА ЕТИЧНЕ ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ДОСЛІДНИЦЬКІЙ І ПУБЛІКАЦІЙНІЙ ДІЯЛЬНОСТІ

Artificial intelligence (AI) is gradually gaining a stable place in our lives, and its use is spreading too quickly in all areas of activity, changing approaches and simplifying the solution of some tasks. AI can help at certain stages of scientific research, but it also poses risks of plagiarism, lack of academic integrity and high ethical standards. The problem of plagiarism and its prevention in the research and publication process has reached a high degree of relevance, especially with the use of AI programs and services. The article presents a comprehensive analysis of scientific sources covering the prevention of plagiarism in scientific publications, the development of AI in research culture, the observance of academic integrity in the university environment, and changes in the assessment of students' academic achievements..Using the GoogleTrends service, based on the search engine's statistical data, it was determined that recently, scientific topics related to ChatGPT have been dominating in the queries of Internet users, while the discourse related to plagiarism, academic integrity, research ethics, and responsible use of AI is not noticeably relevant, and there is an increase in related queries between "plagiarism" and AI tools. The authors substantiate the impact of the use of AI on the responsible attitude to the presentation of research results. After processing the downloaded data in Excel, a hypothesis is formulated regarding the convergence of interests and joint consideration of the problems of ethics in research and responsible use of AI. The analysed data demonstrate that the relevance of ethical and responsible use of artificial intelligence tools in writing scientific manuscripts is extremely high. The article demonstrates how digital tools such as Connected Papers, Inciteful, LitMaps, Scite.Ai, ResearchRabbit, and Semantic Scholar can conveniently visualise documents related to search terms. The article offers recommendations for researchers and editorial board members of scientific journals to promote responsible presentation of research findings in the context of the growing availability and use of AI.

A Survey on Theories and Application for Self Driving Cars on Deep Learning Methods

In today’s, Self-driving cars are one of the hot research topics in technology, which has a great impact on social and economic development. Self-driving cars, powered by deep learning algorithms, represent a groundbreaking fusion of artificial intelligence and transportation technology. Deep learning is also one of the current major areas in artificial intelligence research. It has been widely applied in natural language understanding, image processing, etc. In recent years, more and more deep learning methods have been introduced to the solution in the field of self-driving cars and have achieved excellent results. This survey provides information related to self-driving cars and summarizes the application of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions are analyzed based on deep learning methods, such as lane detection, scene recognition and navigation etc. Also briefly described are some representative approaches to self-driving cars using deep learning methods. Finally, future challenges in applications of deep learning for self-driving cars are described.

A Social Self-Awareness Agent with Embodied Reasoning

In the realm of multi-agent systems, establishing social self-awareness in agents stands as a critical challenge within the broader scope of artificial general intelligence research. Addressing this challenge, in this paper, we draw upon Tomasello’s social self-awareness theory as a foundational framework, and employ Non-Axiomatic Reasoning System (NARS), an artificial general intelligence model, to simulate analogical and recursive reasoning methods used by socially embodied agents. In this paper, we assume that an agent can possess sensorimotor capabilities and physical common sense, it explores the process of constructing a “core self” through embodied reasoning. On this basis, we explore deeper into the construction process of social self-awareness. As a result, we clarify how agents generate common knowledge about themselves through recursive reasoning regarding the mental states of others and communication dynamics. Following this initial step, metaphors are generated utilizing analogical reasoning, intertwining public knowledge with these metaphors to facilitate mutual comprehension between humans and machines. This paper delves into the simulation of perspective representation and recursive reasoning within social embodied agents, employing NARS– an overarching artificial intelligence platform. The experimental results substantiate the viability of constructing a social self-agent within a machine framework grounded in embodied reasoning.

Artificial Intelligence in Orthodontics: Critical Review.

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.

Ethical principles for using the artificial intelligence in research (based on biomedical research). Vestsi Natsyyanal’nai akademii navuk Belarusi

The article is devoted to the relevant issue of modern science and ethical support for scientific research using Artificial Intelligence (AI). Despite a significant number of foreign and domestic publications about AI, the conceptual framework for the ethics of scientific research using AI remains undeveloped. Based on the international recommendations and articles, as well as own research experience and membership of research ethical committees, the authors define and analyze the basic ethical principles for the scientific research using AI. The proposed principles are considered in the context of their practical application in the field of biomedicine, which are connected with protection of the mankind and nature, maintaining the confidentiality of participants‘ data, preventing discrimination, protecting against errors of AI, respecting informed consent, as well as observing the norms of “open science”, mutual trust from developers and users, etc. The application of the proposed principles orients the scientists, the developers of artificial intelligence, ethical committees, realizing review process, all society, to the priority of the humanization of science, respect for man and nature, as well as education of society regarding to AI, the creation of a regulatory framework, ethical recommendations and codes of ethics for the using of AI in scientific research.

Two-Stage Probe-Based Search Optimization Algorithm for the Traveling Salesman Problems

As a classical combinatorial optimization problem, the traveling salesman problem (TSP) has been extensively investigated in the fields of Artificial Intelligence and Operations Research. Due to being NP-complete, it is still rather challenging to solve both effectively and efficiently. Because of its high theoretical significance and wide practical applications, great effort has been undertaken to solve it from the point of view of intelligent search. In this paper, we propose a two-stage probe-based search optimization algorithm for solving both symmetric and asymmetric TSPs through the stages of route development and a self-escape mechanism. Specifically, in the first stage, a reasonable proportion threshold filter of potential basis probes or partial routes is set up at each step during the complete route development process. In this way, the poor basis probes with longer routes are filtered out automatically. Moreover, four local augmentation operators are further employed to improve these potential basis probes at each step. In the second stage, a self-escape mechanism or operation is further implemented on the obtained complete routes to prevent the probe-based search from being trapped in a locally optimal solution. The experimental results on a collection of benchmark TSP datasets demonstrate that our proposed algorithm is more effective than other state-of-the-art optimization algorithms. In fact, it achieves the best-known TSP benchmark solutions in many datasets, while, in certain cases, it even generates solutions that are better than the best-known TSP benchmark solutions.

Automatic Image Caption Generation with Deep Learning

Artificial intelligence research has long focused on the task of automatically defining what is included in a picture or image. This paper presents the CNN and RNN-LSTM models used in the Automatic Caption Generator implementation. It takes into account recent developments in image processing and performing research on translation through automation. The dataset utilized was Flickr8k. We have employed BLEU scores to assess the efficiency of the system that has been described. Based on the scores, one could classify the resulting captions as excellent or awful. The primary uses of this paradigm are virtual assistants, picture indexing, social media, assistive technology for the blind, altering app suggestions, and many more areas. Key Words: Automatic Caption Generator, CNN, RNN-LSTM, Computer vision, Machine translation, Flickr8k dataset, BLEU scores, Performance evaluation, Virtual assistants, Picture indexing, Social media, Assistive technology for the blind, App suggestions.

The Implementation of Artificial Intelligence and its Future Potential

The implementation of artificial intelligence (AI) involves the utilization of various methodologies, including machine learning, deep learning, natural language processing, and robotics. These methodologies enable computers to perform tasks that traditionally require human intelligence, such as recognizing patterns in data, understanding natural language, and making decisions. AI's future potential is vast and spans across numerous industries and applications. In healthcare, AI can assist in disease diagnosis, personalized treatment plans, and drug discovery. In finance, AI algorithms can analyze market trends, detect fraud, and optimize investment portfolios. In transportation, AI powers autonomous vehicles, optimizing routes and reducing accidents. In entertainment, AI-driven recommendation systems personalize content for users. However, realizing this potential requires addressing several challenges. Ethical considerations are paramount, including ensuring fairness, transparency, accountability, and privacy in AI systems. Bias in algorithms must be mitigated to prevent discrimination, and AI decisions must be interpretable and explainable to users. Research efforts are underway to advance AI capabilities while addressing these challenges. This includes developing more robust and efficient algorithms, improving AI safety and security, and exploring applications of AI for social good, such as addressing climate change and promoting inclusivity. Collaboration between academia, industry, and government is crucial for driving AI research forward and ensuring its responsible integration into society. This collaboration facilitates knowledge sharing, resource allocation, and the development of standards and regulations to guide the ethical and equitable deployment of AI technologies. Ultimately, by harnessing the power of AI responsibly, society can benefit from its transformative potential while mitigating potential risks.

Value formation in AI-integrated service system: review and implications on hospitality and tourism research

ABSTRACT The ongoing Fourth Industrial Revolution, which is driven by artificial intelligence (AI) and other disruptive technologies, has attracted a surge of AI studies. Previous reviews primarily offered a bird’s view of AI research and were somewhat one-sided focusing on AI’s bright sides. Reviews on the networks of resources, process dynamics, and divergent outcomes of this complex system are rare, if any. This review article aims to fill this research gap by deconstructing how value is formed in the AI-integrated service system. Guided by the SPAR-4-SLR protocol and interactive value formation framework, this study reviewed and analyzed state-of-the-art AI studies (i.e. 2019–2023) published in six selected top-tier hospitality and tourism journals. This review makes contributions by outlining the resource orchestration, resource integration practices, and divergent outcomes of value formation in the AI-integrated system in hospitality and tourism. Future research directions and implications were also provided to advance the field.

SHIKSHAKENDRA – The Epitome Of Educational Elegance

In the world of computers, artificial intelligence (AI) is a revolutionary field of computer science that aims to create intelligent systems that can mimic cognitive functions similar to those of humans and make predictions, recommendations, and decisions that affect real or virtual environments. AI is intended to facilitate problem-solving. Machine learning, neural networks, and computer vision are just a few of the many methods and tools that are included in artificial intelligence (AI). With the use of these technologies, machines are able to process enormous volumes of data and identify patterns—often rather accurately. Particular objectives and the application of specific techniques, such as reasoning, knowledge representation, planning, learning, and perception, are the focal points of the many subfields of AI study. Researchers in artificial intelligence (AI) have adopted and combined a variety of approaches to solve problems, such as formal logic, artificial neural networks, search and mathematical optimization, and approaches based on statistics, probability, and economics. Philosophy, neurology, and many other disciplines are also incorporated. Subproblems have been identified within the generation problem of making or imitating intelligence. Key Words: Learning, Chatbot, Physical disabled students, Problem-solving, Planning, Learning, Subscription, Courses, Downloads.

Advances in artificial intelligence in thyroid-associated ophthalmopathy.

Thyroid-associated ophthalmopathy (TAO), also referred to as Graves' ophthalmopathy, is a medical condition wherein ocular complications arise due to autoimmune thyroid illness. The diagnosis of TAO, reliant on imaging, typical ocular symptoms, and abnormalities in thyroid function or thyroid-associated antibodies, is generally graded and staged. In recent years, Artificial intelligence(AI), particularly deep learning(DL) technology, has gained widespread use in the diagnosis and treatment of ophthalmic diseases. This paper presents a discussion on specific studies involving AI, specifically DL, in the context of TAO, highlighting their applications in TAO diagnosis, staging, grading, and treatment decisions. Additionally, it addresses certain limitations in AI research on TAO and potential future directions for the field.

Exploring the Limits of Artificial Intelligence for Referencing Scientific Articles.

To evaluate the reliability of three artificial intelligence (AI) chatbots (ChatGPT, Google Bard, and Chatsonic) in generating accurate references from existing obstetric literature. Between mid-March and late April 2023, ChatGPT, Google Bard, and Chatsonic were prompted to provide references for specific obstetrical randomized controlled trials (RCTs) published in 2020. RCTs were considered for inclusion if they were mentioned in a previous article that primarily evaluated RCTs published by the top medical and obstetrics and gynecology journals with the highest impact factors in 2020 as well as RCTs published in a new journal focused on publishing obstetric RCTs. The selection of the three AI models was based on their popularity, performance in natural language processing, and public availability. Data collection involved prompting the AI chatbots to provide references according to a standardized protocol. The primary evaluation metric was the accuracy of each AI model in correctly citing references, including authors, publication title, journal name, and digital object identifier (DOI). Statistical analysis was performed using a permutation test to compare the performance of the AI models. Among the 44 RCTs analyzed, Google Bard demonstrated the highest accuracy, correctly citing 13.6% of the requested RCTs, whereas ChatGPT and Chatsonic exhibited lower accuracy rates of 2.4 and 0%, respectively. Google Bard often substantially outperformed Chatsonic and ChatGPT in correctly citing the studied reference components. The majority of references from all AI models studied were noted to provide DOIs for unrelated studies or DOIs that do not exist. To ensure the reliability of scientific information being disseminated, authors must exercise caution when utilizing AI for scientific writing and literature search. However, despite their limitations, collaborative partnerships between AI systems and researchers have the potential to drive synergistic advancements, leading to improved patient care and outcomes. · AI chatbots often cite scientific articles incorrectly.. · AI chatbots can create false references.. · Responsible AI use in research is vital..

Crafting explainable artificial intelligence through active inference: A model for transparent introspection and decision-making

This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.