Relevance Of Artificial Intelligence Research Articles

NON-ITERATIVE CALCULATION OF PARAMETERS OF A LINEAR CLASSIFIER WITH A THRESHOLD ACTIVATION FUNCTION

The relevance of artificial intelligence (AI) systems is growing every year. AI is being introduced into various fields of activity. One of the main technologies used in AI is artificial neural networks (hereinafter referred to as NN). With the help of neural networks, a huge class of problems is solved, such as classification, regression, autoregression, clustering, noise reduction, creating a vector representation of objects, and others. In this work, we consider the simplest case of operation of one neuron with the Heaviside activation function, we also consider fast ways to train it, and we reduce the learning problem to the problem of finding the normal vector to the separating hyperplane and the displacement weight. One of the promising areas for training NN is non-iterative training, especially in the context of processing and analyzing high-dimensional data. This article discusses a method of non-iterative learning, which allows you to greatly (by 1-2 orders of magnitude) speed up the training of one neuron. The peculiarity of the approach is to determine the hyperplane separating two classes of objects in the feature space, without the need for repeated recalculation of weights, which is typical for traditional iterative methods. Within the framework of the study, special attention is paid to cases when the main axes of the ellipsoids describing the classes are parallel. The function pln is defined to calculate the distances between objects and the centers of their classes, based on which the non-normalized normal vector to the hyperplane and the displacement weight are calculated. In addition, we provide a comparison of our method with support vector machines and logistic regression.

Artificial Intelligence (AI) Usage and Its Influence to the Students’ Academic Writing: A Quantitative – Correlation Investigation

This study dealt with the artificial intelligence (AI) usage and its influence on students’ academic writing. The primary goal of the study was to determine the level of artificial intelligence (AI) usage and students’ academic writing in terms of their respective indicators, the significant difference between artificial intelligence (AI) usage and students’, and what domains of artificial intelligence (AI) substantially influenced students’ academic writing. Also, this study utilized a quantitative-correlational design with 335 participants among Junior High School and Senior High School students in Lorenzo S. Sarmiento Sr. National High School. The average weighted mean, Pearson R, and multiple regression analysis were the statistical tools used in this study. Along with this, the results showed a high level of artificial intelligence (AI) usage in terms of satisfaction, AI literacy, relevance of AI, and confidence. Likewise, results showed a high level of students’ academic writing in terms of usefulness, ease of use, and attitude towards usage. In addition, there was a high correlation, and a significant relationship between artificial intelligence (AI) usage and its influence on the students’ academic writing. Hence, this led to the rejection of the null hypothesis. The domains of artificial intelligence (AI) usage that influenced students’ academic writing are the relevance of AI, confidence, and AI literacy. While satisfaction, as a domain of artificial intelligence (AI) usage, had no significant influence on students’ academic writing. Thus, educational institutions could harness the positive influence of artificial intelligence (AI) on students’ academic writing, creating a more technologically advanced, collaborative, and effective learning environment while encouraging students to participate in AI-assisted activities contributing to better progress and improved self-regulated learning as they interacted with AI tools and their peers in an academic setting.

Relevance Of Artificial Intelligence In Healthcare: An Exhaustive Review

Relevance Of Artificial Intelligence In Healthcare: An Exhaustive Review

ИССЛЕДОВАНИЕ ВОСТРЕБОВАННОСТИ ПРИМЕНЕНИЯ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ В ЦИФРОВОЙ ЭКОНОМИКЕ

The paper investigates the main ways of studying the digital economy using machine learn-ing methods and assesses the degree of their application in different sectors of the economy. Differ-ent points of view on the definition of the concepts of "digital economy" and "machine learning" are considered. The level of development and relevance of artificial intelligence and machine learning, as well as their multi-purpose use in various spheres of society are noted. The types of tasks, for the solution of which by means of machine learning methods the maximum efficiency and productivity are provided. The advantages and disadvantages of using machine learning methods are highlighted and disclosed in more detail. Based on the conducted collection and analysis of reporting data pro-vided by the Federal State Statistics Service "Rosstat", the analysis, synthesis and correlation of indi-cators used to describe the position of machine learning methods in the digital economy are carried out. The most demanded areas of the digital economy from the point of view of applying machine learning algorithms were identified, each sector of which was hierarchically classified depending onthe relevance of applying machine learning methods in it. The ways of categorizing machine learning methods into groups are described. The results of the study allow us to draw conclusions about the position of machine learning methods in the modern digital economy, the importance of their appli-cation and the need to train specialists in this field.

Up To Date: Automatic Updating Knowledge Graphs Using LLMs

Maintaining up-to-date knowledge graphs (KGs) is essential for enhancing the accuracy and relevance of artificial intelligence (AI) applications, especially with sensitive domains. Yet, major KGs are either manually maintained (e.g., Wikidata) or infrequently rebuilt (e.g., DBpedia & YAGO). Thus, they contain many outdated facts. The rise of Large Language Models (LLMs) reasoning and Augmented Retrieval Generation approaches (RAG) gives KGs an interface to other trusted sources. This paper introduces a methodology utilizing Large Language Models (LLMs) to validate and update KG facts automatically. In particular, we utilize LLM reasoning capabilities to determine potentially outdated facts. After that, we use RAG techniques to generate an accurate fix for the fact. Experimental results on several LLMs and real-world datasets demonstrate the ability of our approach to propose accurate fixes. In addition, our experiments highlight the efficacy of few-shot prompts over zero-shot prompts.

POSSIBILITIES OF MODERN COMPUTER TECHNOLOGIES USING ARTIFICIAL INTELLIGENCE IN CREATING FINE ART OBJECTS

This article explores the possibilities of current technologies with the addition of artificial intelligence to the creation of different types of creative arts. The scope of application of artificial intelligence technology in the formation and creation of creative objects has been explored. The main advantages of using artificial intelligence in creating creativity are identified. Attention is paid to the methods and fundamentals of shaping and creating creative objects, artificial intelligence in comparison with humans. The main functions and capabilities of neural networks in creating and generating a concept for a creative product have been studied. The shortcomings and imperfections in the creation and formation of a work of art using modern artificial intelligence technologies have been identified. The question of the genius and uniqueness of human thought is considered in the search for primary ideas and the creation of a concept in comparison with algorithms for the formation of ideas made using computer technology and neural networks. The current range of artificial intelligence functions has been analyzed, which can help simplify most of the tasks required when creating primary visualization, a sketch part for the subsequent implementation of a design project. The algorithm and its features of forming a query are considered to obtain the final visualization and the product of artificial intelligence. The relevance of artificial intelligence and its influence on the development of creativity in various fields of art is considered. The factor of influence of computer technologies and neural networks on the media space and society as a whole has been identified. The relevance of the discussion on the impact of artificial intelligence and neural networks on the labor market is analyzed. The functions and capabilities of artificial intelligence are examined in detail, based on the current “Midjorney” neural network. The influence of the neural network on the scope of activity of interior designers and the formation of internal spaces is considered. The methods of using a neural network in the formation of landscape design and urban planning are discussed in detail. The range of applications and possibilities of artificial intelligence in the creation of fine art and sculpture has been explored.

AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

The use of artificial intelligence (AI) is becoming increasingly important in various domains, making education about AI a necessity. The interdisciplinary nature of AI and the relevance of AI in various fields require that university instructors and course developers integrate AI topics into the classroom and create so-called domain-specific AI courses. In this paper, we introduce the “AI Course Design Planning Framework” as a course planning framework to structure the development of domain-specific AI courses at the university level. The tool evolves non-specific course planning frameworks to address the context of domain-specific AI education. Following a design-based research approach, we evaluated a first prototype of the tool with instructors in the field of AI education who are developing domain-specific courses in this area. The results of our evaluation indicate that the tool allows instructors to create domain-specific AI courses in an efficient and comprehensible way. In general, instructors rated the tool as useful and user-friendly and made recommendations to improve its usability. Future research will focus on testing the application of the tool for domain-specific AI course developments in different domain contexts and examine the influence of using the tool on AI course quality and learning outcomes.

IoT‐Based Voice‐Controlled Smart Homes with Source Separation Based on Deep Learning

The widespread availability of cutting‐edge computer technologies has shed light on the relevance of artificial intelligence (AI) applications in almost all sectors of the economy. As a result of the incorporation of voice control processing into many Internet of Things (IoT) devices, many of these IoT devices may be operated using spoken commands. The environment that is controlled by speech may include several devices, each of which may be used for a separate activity; yet, all of the devices may collect and process the same command at the same time. This may be the case if the devices can communicate with one another. Because other devices may choose to ignore orders that are intended for particular devices if those devices are not equipped to deal with those orders, only the device that is designed to carry out the activity and process the command will be able to carry out the activity. This is because only the device that is designed to carry out the activity and process the command will be able to carry out the activity. On the other hand, when all of the voice‐controlled devices capture the command through the microphone, there is a greater chance that it will mix with other sounds coming from a variety of sources. This is because the microphone is being used to capture the command from all of the voice‐controlled devices. These noises may include those that are emanating from the television, music systems, and other sounds that are created by activities taking on inside the family, among other things. During the identification of instructions via processing, any blending of other sounds that are not the primary command is regarded as noise and has to be deleted. This is because any such blending is deemed to be noise. The direction of arrival (also known as DOA) of the sound waves is given primary consideration by this approach. This is done at the same time as the performance of the system, and the proposal for it are being evaluated. Based on the angle of arrival estimate, a specific room impulse response (RIR) from a collection of defined RIR is identified as a room acoustic characteristic, and source separation is carried out using the technique of independent component analysis (ICA). Following the completion of the analysis of the signals produced by the split command speech, the characteristics of the speech are retrieved from the signals. The Mel‐frequency cepstral coefficients (MFCC) approach is used so that the operation of feature extraction may be carried out. This is the goal of the technique. Following that, a support vector machine classifier is used to the data in order to further split these characteristics into a large range of distinct groups. Comparisons are made between the performance of the SVM classifier and the performance of a large number of different classifiers, including decision trees, which are often used in applications that incorporate machine learning (DT). After analyzing its performance, the multiclass SVM classifier is found to have an accuracy of 91%, according to the conclusions of the study. Utilizing a classifier that is based on a probabilistic neural network, which is sometimes referred to as a PNN, is one way in which the accuracy of future classifications may be enhanced. This particular classifier is made up of three layers: one layer of gated recurrent units (GRU), one layer of long short‐term memory (LSTM), and one layer that integrates the two of those different kinds of memory. This classification seems to have obtained an accuracy of 94.5 percent, which is higher than the classification accuracy attained by the multiclass SVM classifier.

Implementation of Artificial Intelligence in Agriculture to Optimize Irrigation

Abstract. The relevance of artificial intelligence in agriculture is substantiated for irrigation optimization. (Research purpose) To report on the progress made over the past few years in the application of artificial intelligence to optimize crop irrigation. (Materials and methods) The review focuses on the most salient facts and important scientific information on the application of artificial intelligence in crop production. The review is based on Various databases (Google Scholar, PubMed, Science Direct, SciFinder, Web of Science, RSCI) and online sources (Research Gate, Springer Nature Open Access, Wiley Online Library). It is shown how the integration of machine learning models can provide intelligent irrigation management. The review reports on the research trends and applicability of machine learning methods, as well as the deployment of developed machine learning models for sustainable irrigation management. (Results and discussion) Mobile and web platforms are shown to be able to facilitate intelligent irrigation management. Machine learning proves to be one of the central areas of artificial intelligence helping researchers to work more creatively and efficiently. The review notes the problems of introducing artificial intelligence in crop production and specifies the future research areas in the machine learning implementation and digital farming solutions. (Conclusions) The relevance of the intelligent system in irrigation and water management is proved for sustainable agriculture. It is revealed that, despite the extensive literature available, machine learning modeling for crop irrigation management is still in its infancy. The countries leading in this area are China, the United States and Australia.

An Interdisciplinary Systematic Review on Artificial Intelligence in Management and Technology

Research on artificial intelligence has escalated in recent years as an increasing number of researchers are systematically involved in explaining the application, impact, and relevance of artificial intelligence across fields. Although the number of research studies with a core focus on artificial intelligence has considerably increased over the years, little is known about how scholars from different fields view, use, and interpret artificial intelligence. As a consequence, artificial intelligence has become a buzzword with wide and multiple interpretations and applications across disciplines. This study aims at (a) providing a comprehensive understanding of the term artificial intelligence and (b) explaining whether the term relates to increased convergence/divergence across fields (i.e., technical vs. nontechnical/management studies). We answer these questions by conducting a systematic literature review and employing advanced bibliometric and topic modeling methods on a total of 791 studies drawn from leading technical and nontechnical/management scholarly journals over the period 2006 – 2021.

Impact and Feasibility of harnessing AI and ML in the realm of Cybersecurity to detect Network Intrusions A Review

Remarkable advances in cyberspace, have amassed a magnanimous set of Internet users worldwide. While people engage in various activities and use the web for various needs, the prospective fear of cyber attacks, crime and threats is indubitable. Though a plethora of preventive measures are in use, it is impossible to circumvent cyber threats completely. Cybersecurity is a domain that deals with prevention of cyber attacks by use of effective precautionary and remedial measures. With the advent of Artificial Intelligence (AI) and Machine Learning (ML) and its profound scope in contemporary technical innovations, it is a critical necessity to inculcate its techniques in enhancement of existing cybersecurity techniques. This paper offers a detailed review of the concepts of cybersecurity, commonly encountered cyber attacks, the relevance of AI and ML in cybersecurity along with a comparative performance analysis of distinct ML algorithms to combat network anomaly detection and network intrusion detection.

Role of artificial intelligence in health communication

Artificial intelligence is a transformational technology of the digital age and increasingly capturing the world. Apart from robotics, this technology is also useful in the health sector. Smartwatches are a good example of artificial intelligence and health communication. This device provides you with some specific information like blood pressure, oxygen level, sleep monitoring, etc. which is very useful for a common man to track his/her health. This device communicates to you about your primary health issue and informs you to visit a doctor. So, in this study, researchers explored and examined the role and relevance of artificial intelligence in health communication, Research evaluates the level of awareness among users and non-users of smartwatches AI technology and finds out the effectiveness of using smartwatches. For data gathering, researchers will use a survey method. In this study, a quantitative approach was used and a random sampling method has been adopted for the selection of smartwatch users.

Relevance of Artificial Intelligence in a Retail Pharma Store: A Review

Whole pharmaceutical areas needed various innovative and scientific solutions to solve the current problems related to cGMP practices, production, documentation, regulatory body requirements (USFDA, WHO, EU-GMP, PICS), quality of product, etc. To overcome such problems, different computer system software plays an important role by monitoring and maintaining the current practices of the pharma industry. Installing such software will help the pharma industry to improve their cGMP practices and fulfillment the regulatory body’s requirements. This article gives overview on different types of software used in the pharmaceutical industry and hospitals. It also covers importance and their need in the pharma industry, as well as the meaning of software used in hospitals.

Teachers’ readiness and intention to teach artificial intelligence in schools

The emergence of artificial intelligence (AI) as a subject to be incorporated into K-12 educational levels places new demand on relevant stakeholders, especially teachers that drive the teaching and learning process. It is therefore important to understand how ready teachers are to teach the emerging subject as the success of AI education would probably be closely dependent on the readiness of teachers. As a result, this study presents an insight into factors influencing the behavioural intention and readiness of Nigerian in-service teachers to teach artificial intelligence. A total of 368 teachers, from elementary to high school participated in the study. We utilised quantitative methodology using variance-based structural equation modelling to understand the relationship among the eight variables (AI anxiety, perceived usefulness, AI for social good, Attitude towards using AI, perceived confidence in teaching AI, relevance of AI, AI readiness, and behavioural intention) considered in the study. The result indicated that confidence in teaching AI predicts intention to teach AI while AI relevance strongly predicts readiness to teach AI. While other factors influence the teaching of AI, anxiety and social good could not predict teachers' intention and readiness to implement AI in classrooms respectively. We discussed the implication of our findings in relation to AI implementation in schools and highlight future directions.

Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images

Early classification of brain tumors from magnetic resonance imaging (MRI) plays an important role in the diagnosis of such diseases. There are many diagnostic imaging methods used to identify tumors in the brain. MRI is commonly used for such tasks because of its unmatched image quality. The relevance of artificial intelligence (AI) in the form of deep learning (DL) has revolutionized new methods of automated medical image diagnosis. This study aimed to develop a robust and efficient method based on transfer learning technique for classifying brain tumors using MRI. In this article, the popular deep learning architectures are utilized to develop brain tumor diagnostic system. The pre-trained models such as Xception, NasNet Large, DenseNet121 and InceptionResNetV2 are used to extract the deep features from brain MRI. The experiment was performed using two benchmark datasets that are openly accessible from the web. Images from the dataset were first cropped, preprocessed, and augmented for accurate and fast training. Deep transfer learning models are trained and tested on a brain MRI dataset using three different optimization algorithms (ADAM, SGD, and RMSprop). The performance of the transfer learning models is evaluated using performance metrics such as accuracy, sensitivity, precision, specificity and F1-score. From the experimental results, our proposed CNN model based on the Xception architecture using ADAM optimizer is better than the other three proposed models. The Xception model achieved accuracy, sensitivity, precision specificity, and F1-score values of 99.67%, 99.68%, 99.68%, 99.66%, and 99.68% on the MRI-large dataset, and 91.94%, 96.55%, 87.50%, 87.88%, and 91.80% on the MRI-small dataset, respectively. The proposed method is superior to the existing literature, indicating that it can be used to quickly and accurately classify brain tumors.

Investigation of SiO2 Nanoparticle Retention in Flow Channels, Its Remediation Using Surfactants and Relevance of Artificial Intelligence in the Future

In this study, the effect of these variables on commercial silica NP retention was presented in a fabricated flow model considering only the physical adsorption aspects of silica NP retention. From our observations, it was established that while silica NP concentration, flow rate and salt are key variables in influencing silica NP agglomeration and retention, the effect of temperature was highly subdued. The effect of salt-induced agglomeration was particularly severe at moderate salinity (≈4 wt% NaCl). To mitigate the effect of salt-induced agglomeration, a commonly used anionic surfactant, sodium dodecyl sulfate (SDS) was added to the solution and the silica NP retention was tabulated. An amount of 0.3 wt% SDS was found to negate salt-induced agglomeration significantly, paving the way for use of silica NP solutions, even in the presence of saline conditions. A section on the prospective use of artificial intelligence for this purpose has been included. This study is useful for understanding NP retention behaviour, especially in the presence of salinity and its mitigation using surfactants, in flow applications.

The perceptions of medical physicists towards relevance and impact of artificial intelligence.

Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control(QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.

인공지능과 인문정신: 디지털 시대의 인문학의 대중화

Martin Heidegger, in his widely-known essay, “The Question Concerning the Technology,” asks prophetic questions anticipating the era of artificial intelligence and posthumanism, defines technology in terms of an “instrumentum” of Latin etymology which combines the double meanings of “a means to an end” and “human activity,” and articulates the “instrumentality” in the context of the Aristotelian causality of the four causes. The instrumentality refers to the use of the tool by setting it up. Heidegger argues that the instrumentality is the condition that brings about the right relationship between humans and technology, the relationship in which technology depends on how humans properly control the means. Thus, technology is inevitably instrumental and anthropological. The importance of the ethical value of this paper lies in the way humans dominate technology psycho-somatically. As the technology threatens to get out of human control, the human will to dominate the technology becomes urgent. In this context, this paper traces the development of AI in relation to the robotics in a genealogical way, in particular, the developmental process in which humanities, cognitive science, brain science, and robotics are rapidly and broadly integrated based on artificial intelligence and big data. Then, this paper showcases the analysis of how humanities thinking methods, especially literary imagination and philosophical thinking methods, relate to the popularization of humanities in the digital age in the context of written language and humanities through the coding process. The main objective of this paper is to examine the significance and the relevance of artificial intelligence and human intelligence to the human community at large that embodies the collective humanities spirit.

A Transfer Learning approach for AI-based classification of brain tumors

Classification of Brain Tumor (BT) is a vital assignment for assessing Tumors and making a suitable treatment. There exist numerous imaging modalities that are utilized to identify tumors in the brain. Magnetic Resonance Imaging (MRI) is generally utilized for such a task because of its unrivaled quality of the image and the reality that it does not depend on ionizing radiations. The relevance of Artificial Intelligence (AI) in the form of Deep Learning (DL) in the area of medical imaging has paved the path to extraordinary developments in categorizing and detecting intricate pathological conditions, like a brain tumor, etc. Deep learning has demonstrated an astounding presentation, particularly in segmenting and classifying brain tumors. In this work, the AI-based classification of BT using Deep Learning Algorithms are proposed for the classifying types of brain tumors utilizing openly accessible datasets. These datasets classify BTs into (malignant and benign). The datasets comprise 696 images on T1-weighted images for testing purposes. The projected arrangement accomplishes a noteworthy performance with the finest accuracy of 99.04%. The achieved outcome signifies the capacity of the proposed algorithm for the classification of brain tumors.

Future Vision of Interactive and Intelligent TV Systems using Edge AI

Recently the Brazilian DTV system standards have been upgraded, called TV 2.5, in order to provide a better integration between broadcast and broadband services. The next Brazilian DTV system evolution, called TV 3.0, will address more deeply this convergence of TV systems not only at low-level network layers but also at the application layer. One of the new features to be addressed by this future application layer is the use of Artificial Intelligence technologies. Recently, there have been practical applications using Artificial Intelligence (AI) deployed to improve TV production efficiency and correlated cost reduction. The success in operationalize and evaluate these applications is a strong indication of the interest and relevance of AI in TV. This paper presents TeleMídia Lab’s future vision on interactive and intelligent TV Systems, with particular focus on edge AI. Edge AI means use in-device capabilities to run AI applications instead of running them in cloud.