Scope Of Artificial Intelligence Research Articles

The scope of artificial intelligence in retinopathy of prematurity (ROP) management.

Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the dependence on qualified professionals/experts for screening the large at-risk population, especially in the Indian scenario. Deep learning involves learning without being explicitly told what to focus on and utilizes several layers of artificial neural networks (ANNs) to create a robust algorithm that is capable of high-complexity tasks. Convolutional neural networks (CNNs) are a subset of ANNs that are particularly useful for image processing as well as cognitive tasks. Training of these algorithms involves inputting raw human-labeled data, which are then processed through the algorithm's multiple layers and allow CNN to develop their own learning of image features. AI systems must be validated using different population datasets since the performance of the AI system would vary according to the population. Indian datasets have been used in AI-based risk model that could predict whether an infant would develop treatment-requiring retinopathy of prematurity (ROP). AI also served as an epidemiological tool by objectively showing that a higher ROP severity was in Neonatal intensive care units (NICUs) that did not have the resources to monitor and titrate oxygen. There are rising concerns about the medicolegal aspect of AI implementation as well as discussion on the possibilities of catastrophic life-threatening diseases like retinoblastoma and lipemia retinalis being missed by AI. Computer-based systems have the advantage over humans in not being susceptible to biases or fatigue. This is especially relevant in a country like India with an increased rate of ROP and a preexisting strained doctor-to-preterm child ratio. Many AI algorithms can perform in a way comparable to or exceeding human experts, and this opens possibilities for future large-scale prospective studies.

Advances in the co-production of biosurfactant and other biomolecules: statistical approaches for process optimization.

An efficient microbial conversion for simultaneous synthesis of multiple high-value compounds, such as biosurfactants and enzymes, is one of the most promising aspects for an economical bioprocess leading to a marked reduction in production cost. Although biosurfactant and enzyme production separately have been much explored, there are limited reports on the predictions and optimization studies on simultaneous production of biosurfactants and other industrially important enzymes, including lipase, protease, and amylase. Enzymes are suited for an integrated production process with biosurfactants as multiple common industrial processes and applications are catalysed by these molecules. However, the complexity in microbial metabolism complicates the production process. This study details the work done on biosurfactant and enzyme co-production and explores the application and scope of various statistical tools and methodologies in this area of research. The use of advanced computational tools is yet to be explored for the optimization of downstream strategies in the co-production process. Given the complexity of the co-production process and with various new methodologies based on artificial intelligence (AI) being invented, the scope of AI in shaping the biosurfactant-enzyme co-production process is immense and would lead to not only efficient and rapid optimization, but economical extraction of multiple biomolecules as well.

Scope of artificial intelligence in airway management.

The evolution of artificial intelligence (AI) systems in the field of anaesthesiology owes to notable advancements in data processing, databases, algorithmic programs, and computation power. Over the past decades, its accelerated progression has enhanced safety in anaesthesia by improving the efficiency of equipment, perioperative risk assessments, monitoring, and drug administration systems. AI in the field of anaesthesia aims to improve patient safety, optimise resources, and improve the quality of anaesthesia management in all phases of perioperative care. The use of AI is likely to impact difficult airway management and patient safety considerably. AI has been explored to predict difficult intubation to outperform conventional airway examinations by integrating subjective factors, such as facial appearance, speech features, habitus, and other poorly known features. This narrative review delves into the status of AI in airway management, the most recent developments in this field, and its future clinical applications.

Искусственный интеллект в банках: первые результаты, препятствия и риски

The use of artificial intelligence is becoming one of the development trends in the financial industry. Many companies have already mastered the first directions of the application of artificial intelligence and are now looking for new ways to use this technology. The article analyzes the current state, development trends, prospects and risks of introducing artificial intelligence technologies into the work processes of Russian banks. The dynamics of the Russian artificial intelligence technology market for 2022 are presented, examples of practical business solutions using these technologies in marketing are given, and their advantages and disadvantages are highlighted. The purpose of the article is to identify the risks and prospects for the development of artificial intelligence technologies in the banking sector based on an assessment of the current results of using this technology in banks and the opinions of their clients. Based on the author's survey of respondents, a study of attitudes towards such changes was conducted. The survey results showed that the younger generation is ready to interact with artificial intelligence systems, but at the same time there is still distrust in the solutions and products offered on their basis. The first results of introducing artificial intelligence technologies into business processes are presented using the example of Sberbank, which, being the largest Russian bank, is actively developing and implementing this technology into its systems and products. Thus, the scope of artificial intelligence will expand and cover new directions, facilitating labor-intensive and routine tasks for personnel. Companies and banks without the introduction of artificial intelligence into business processes may lose their competitiveness in the future.

The Process of AI-Aided Drug Design

Artificial Intelligence (AI) is a growing field in today’s world and plays a part in many industries today. Its role in drug design and the biological sciences has begun to expand in recent years. DeepChem is an open source tool that explores and employs the methods behind drug design. The tool’s process and end result will be indicative of how well AI can perform the job of drug discovery and how much it can expedite the process, as well as reveal the future of tools like DeepChem. DeepChem handles everything from data processing to fitting AI models to performing predictions on proposed molecules. By applying DeepChem and reviewing it, we will be revealing AI’s power and limitations in biomedical chemistry and technology. In addition, other AI tools, such as Chemistry 42 and inClinico by Insilico that achieve other parts of the drug design process. Completing a comprehensive review of these methods will provide an overview of what can be improved and the scope of AI as a big money-maker and solution in the biomedical field. The synthesis of drugs is a complicated process that can be simplified by AI tools. This paper explores how AI tools operate and their limitations in the medicinal world.

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students.

In the AI of the Beholder—A Qualitative Study of HR Professionals’ Beliefs about AI-Based Chatbots and Decision Support in Candidate Pre-Selection

Despite the high potential of artificial intelligence (AI), its actual adoption in recruiting is low. Explanations for this discrepancy are scarce. Hence, this paper presents an exploratory interview study investigating HR professionals’ beliefs about AI to examine their impact on use cases and barriers and to identify the reasons that lead to the non-adoption of AI in recruiting. Semi-structured interviews were conducted with 25 HR professionals from 21 companies. The results revealed that HR professionals’ beliefs about AI could be categorised along two dimensions: (1) the scope of AI and (2) the definition of instruction. “Scope of Al” describes the perceived technical capabilities of AI and determines the use cases that HR professionals imagine. In contrast, the “definition of instruction” describes the perceived effort to enable an AI to take on a task and determines how HR professionals perceive barriers to Al. Our findings suggest that HR professionals’ beliefs are based on vague knowledge about AI, leading to non-adoption. Drawing on our findings, we discuss theoretical implications for the existing literature on HR and algorithm aversion and practical implications for managers, employees, and policymakers.

Quantification of Training in Human Thought using Chaos Theory: Degree of Decreasing Entropy as an Indicator

Many analytical methods used to quantify the technical training of human resources have been reported, but they are poor at assessing the training of human thought. Chaos theory and the degree of decreasing entropy have not yet been used in such analytics, although all phenomena—except for those in a completely fixed state (e.g., mathematics, historical facts, and true natural laws) or a random state (e.g., dialing a random digit)—are based on chaos theory. Chaos theory must therefore be considered in the quantification of the technical and intellectual training of human resources because human thinking is involved. This report compares a method for chaos theoretical quantification of human resource training to methods that do not involve chaos theory. The development of the ability to change to a fixed state from a chaotic state beyond the Feigenbaum point is the goal for the training in thinking; this process, which involves a decrease in entropy, is the most difficult, and important process in such training. In a chaotic state with an infinite number of solutions, humans can never show their own state using any method. In many reports, each goal in a fixed state is considered after thinking has been rearranged; however, a chaotic state in human thinking can never be expressed using indices such as productivity, efficiency, or job satisfaction. Indeed, many reported results are merely a part of human resource training, but the change of entropy in the fixed state is small (this corresponds to the “creativity” of artificial intelligence). By contrast, the change in entropy from a chaotic state to a fixed state is large, and this corresponds to human intuitive. Considered in terms of chaos theory, goal achievement for thinking must therefore be quantified in terms of the degree of decreasing entropy (i.e., the problem-solving speed, and degree of problem difficulty). This type of problem-solving speed is not about achieving ta goal but about creating a new idea. The concrete methods considered are the Schedule for the Evaluation of Individual Quality of Life-Direct Weighting method, the Kawakida Jiro method, and the Mandala chart. Based on the findings presented here, considering entropy change in the chaotic state should become an index for evaluating the creation of a new idea instead of the repetition of an existing idea, as this type of thinking is beyond the scope of artificial intelligence. Given that almost all natural phenomena, including human thinking, are based on chaos theory, using this theory can promote scientific development in all academic fields.

A Study on Scope of Artificial Intelligence in Diagnostic Medicine

Artificial Intelligence techniques are used to improve patient care and health systems. The use of machine learning and deep learning in healthcare is indeed prevalent, and it's fascinating to see how different medical data sources can be combined to diagnose diseases accurately. The variety of diseases that can be diagnosed using Artificial Intelligence (AI) techniques is impressive, and it’s interesting to note that different medical imaging datasets are used for feature extraction and classification to make predictions. This highlights the potential of AI in healthcare to assist healthcare providers in identifying diseases and providing appropriate treatments. It is also encouraging to see that AI can enhance the patient experience in hospitals and speed up rehabilitation at home. This can help to improve patient outcomes and reduce healthcare costs. Overall, it's evident that AI has the potential to revolutionize the healthcare industry and improve patient care. As the technology continues to evolve, it will be interesting to see how it is further applied in healthcare and the impact it has on patient outcomes. In this study, the scope of AI in diagnostic medicine has been analysed and summarized.

Directions of application of artificial intelligence in digital marketing communications

The digital transformation of modern economic life has found its reflection in the field of marketing communications, having formed a virtually new form of communication between market entities-digital. A distinctive feature of this form is the personalization of the message, the high selectivity of the consumer’s choice, as well as the possibility of a permanent communication process in which the consumer has the opportunity to independently create content about the company and its product, as well as actively distribute it for their own purposes, sometimes different from the goals of the company. The use of digital marketing communications in the activities of companies provides a number of advantages, such as the infinity and global coverage of the market, the versatility of market impact effects, personalization of communications, etc., which cannot be realized without artificial intelligence technologies. The scope of artificial intelligence in modern companies is extensive. Today, many innovative solutions for the automation of production and other business processes of companies are unthinkable without artificial intelligence. Artificial intelligence is «busy» in routine marketing processes, for example, accompanying the process of purchasing and delivering goods to the consumer, etc. The subject of research in this article is the possibility of creating content, implementing the targeting process and other areas of application in digital marketing communications of artificial intelligence. The object of the study is modern developments in the field of digital marketing communications, which contribute to the formation of effective interactions with the consumer by «fixing» them in behavioral models.

Reviewing Learning and Teaching Content in the Scope of Artificial Intelligence: for Humanities and Social Sciences Majors

Social progress is largely determined by the degree to which digital reality is mastered by society as well as by the development and implementation of innovative technologies related to the use of artificial intelligence. Of particular relevance are issues related to the training of a new generation of specialists, who will be able not only to harmoniously fit into the current digital contexts of social life but also purposefully improve, develop and transform their professional digital tools. It is required to set and solve the tasks of developing students’ competences at a high-quality level, which implies pedagogical specification of the modern educational content. The aim of the study is to elaborate a didactic apparatus for revising the modern educational content for students majoring in the humanities and social sciences, taking into account the current contexts of professional activity in digital reality. The authors have formulated a hypothesis that, in order to prepare humanities students to solve the ever more complex tasks of professional activity and communication in digital reality, it is necessary to update the educational content on an interdisciplinary basis, bringing it into line with the digital contexts of professional activity in this field and introducing a new content outline. This can be done if the following tasks are solved: (1) to define the current areas of work of a specialist in the humanities and social sciences in digital reality and, on this basis, create a digital profile for such a specialist, i.e., an invariant set of tasks relevant in the context of digitalization as well as the corresponding competences; (2) to determine a set of disciplines that have the greatest potential for implementing the digital profile of such a specialist; (3) to substantiate the project of changes in the educational content for students majoring in the humanities and social sciences; and (4) to conduct an experimental test of the draft changes in this educational content. The nominal data comparison method, namely the McNemar test, was used. The solution of the above tasks allowed us: in theoretical terms, to lay the conceptual foundations for selecting the actual educational content for students majoring in the humanities and social sciences in digital reality and, in practical terms, to propose specific tools for updating the subject content. The obtained results confirmed the hypothesis of the study and ways to update the educational content on an interdisciplinary basis, taking into account the current contexts of the professional activity in the social and humanitarian sphere in digital reality.

Leveraging Artificial Intelligence for Enhanced Inventory Management: A Theoretical Study

This research paper explores the theoretical foundations of utilizing artificial intelligence (AI) in inventory management. Through an in-depth review of existing literature and theoretical frameworks, the paper examines the potential benefits and challenges associated with implementing AI-driven solutions in inventory management systems. Theoretical models and frameworks are analysed to elucidate the role of AI in optimizing various aspects of inventory management, including demand forecasting, stock optimization, supply chain coordination, and cost management. The paper also discusses the potential impact of AI on operational efficiency, customer satisfaction, and financial stability within organizations. Furthermore, it explores theoretical insights into the future trends and scope of AI in inventory management.

FOREST FIRE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Fire outbreaks are a typical occurrence all throughout the world, and the harm they bring to both nature and humans is enormous. In comparison to classic sensor-based fire detection systems, vision-based fire detection systems have recently gained favour. The detection process using image processing techniques, on the other hand, is extremely time consuming. We designed a Convolutional Neural Network-based fire detection technique. Through training with datasets, obtain high-accuracy fire image detection that is consistent with fire detection. In this paper, we propose a system for detecting fire using Convolutional Neural Networks (CNN). This paper critically analyzes the scope of Artificial intelligence for detection with video from CCTV footages. This project uses dataset containing video frames with fire. The data is then preprocessed and use the CNN to build a model to detect fire. The dataset is given as input for validating the algorithm and experiments are noted. This project focus on building high accuracy and cost efficient machine that can be used for fire detection.There are 755 and 244 images in the datasets for fire and non-fire, respectively. There are 999 photos in all. These images were created using fire-related video and some images found on the internet. There are 999 images that have been scaled and reshaped to convert to a training dataset and 999 images that have been resized and reshaped to convert to a testing dataset. Convolution, activation functions, and the max pooling technique are used to train the model. The model is trained by varying batch sizes and epoch values. As a result, we obtain a high accuracy and detection rate. Key words: CNN,fire detection,machine learning.

Базова модель нефункційних характеристик для оцінки якості штучного інтелекту

The subject of the research is the models of artificial intelligence (AI) quality. The current paper develops an AI quality model based on the definition and ordering of its characteristics. Objectives: to develop the principles and justify the sequence of analysis and development of AI quality models as ordered sets of characteristics; to offer models of AI quality for further use, first, the evaluation of individual characteristics and quality in general; to demonstrate the profiling of AI quality models for systems using artificial intelligence. The following results were obtained. The sequence of construction of AI quality models is offered. Based on the analysis of references, a list of AI characteristics was formed and their definitions were harmonized. The general model of AI quality is presented with a description of the step-by-step procedure for the realization of its hierarchical construction. A basic model of AI with abbreviated sets of characteristics is proposed due to its importance. Examples of profiling of quality models for two systems - monitoring of engineering communications and recognition of road signs are given. Conclusions. The study's main result is the development of a quality model for artificial intelligence, which is based on the analysis and harmonization of definitions and dependencies of quality characteristics specific to AI. The selection of characteristics and the construction of the quality model were carried out in such a way to exclude duplication, ensure the completeness of the presentation, as well as to determine the specific features of each characteristic. It is extremely difficult to create a model that would fully meet such requirements, so the presented options should be supplemented and improved considering the rapid development of technologies and applications of AI. The proposed quality models are open and can be supplemented and detailed according to the specific purpose and scope of AI.

Guest Editorial Security-by-Design for Electronic Systems

In a recent Special Section published in IEEE Transactions on Consumer Electronics we introduced smart electronics <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . We presented a set of papers which were selected after rigorous review with a scope of artificial intelligence (AI) or machine learning (ML) approaches used for various levels of system abstractions, from device-level to system-level. In one school of thought energy-smart, security-smart, and response-smart are 3 dimensions of smart electronic systems.

Artificial intelligence and 3D printing technology in orthodontics: future and scope

&lt;abstract&gt; &lt;p&gt;New digital technologies, like in other fields, have revolutionized the health care field and orthodontic practice in the 21&lt;sup&gt;st&lt;/sup&gt; century. They can assist the health care professionals in working more efficiently by saving time and improving patient care. Recent advances in artificial intelligence (AI) and 3D printing technology are useful for improving diagnosis and treatment planning, creating algorithms and manufacturing customized orthodontic appliances. AI accomplishes the task of human beings with the help of machines and technology. In orthodontics, AI-based models have been used for diagnosis, treatment planning, clinical decision-making and prognosis prediction. It minimizes the required workforce and speeds up the diagnosis and treatment procedure. In addition, the 3D printing technology is used to fabricate study models, clear aligner models, surgical guides for inserting mini-implants, clear aligners, lingual appliances, wires components for removable appliances and occlusal splints. This paper is a review of the future and scope of AI and 3D printing technology in orthodontics.&lt;/p&gt; &lt;/abstract&gt;

Is there A Scope of Artificial Intelligence in Dentistry

Is there A Scope of Artificial Intelligence in Dentistry

Scope of Artificial Intelligence in Gastrointestinal Oncology.

Simple SummaryGastrointestinal cancers cause over 2.8 million deaths annually worldwide. Currently, the diagnosis of various gastrointestinal cancer mainly relies on manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. Artificial intelligence (AI) may be useful in screening, diagnosing, and treating various cancers by accurately analyzing diagnostic clinical images, identifying therapeutic targets, and processing large datasets. The use of AI in endoscopic procedures is a significant breakthrough in modern medicine. Although the diagnostic accuracy of AI systems has markedly increased, it still needs collaboration with physicians. In the near future, AI-assisted systems will become a vital tool for the management of these cancer patients.Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.

The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges

AbstractNext‐generation wireless communication networks, in particular, the densified 5G will bring many developments to the existing telecommunications industry. The key benefits will be the higher throughput and very low latency. In this context, the usage of unmanned aerial vehicle (UAV) is becoming a feasible option for deploying 5G services on demand. At the same time, the immense bandwidth potential of mmWave has strengthened its performance in radio communication. In this article, we provide a consolidated synthesis on the role of UAVs and mmWave in 5G, emphasis on recent developments and challenges. The review focuses on UAV relay architectures, identifies the relevant problems and limitations in the deployment of UAVs using mmWave in both access and backhaul links simultaneously. There is a critical analysis of the optimum placement of the UAVs as a relay with a focus on the mmWave band. The distinctive rich characteristics of the mmWave propagation and scattering are presented. We also synthesis mmWave path loss models. Then, the scope of artificial intelligence and machine learning techniques as an efficient solution for combating the dynamic and complex nature of UAV‐based cellular communication networks are discussed. In the end, security and privacy issues in UAV‐based cellular network are spotlighted. It is believed that the literature discussed, and the findings reached in this article are of significant importance to researchers, application engineers and decision‐makers in the designing and deployment of UAV‐supported 5G network.

Los retos del desarrollo ético de la Inteligencia Artificial

Los constantes avances tecnológicos han provocado una verdadera revolución en nuestro modus vivendi que, todavía, impactan de forma exponencial a la sociedad como un todo. Vivimos en la era de la información y, en ella, uno de los principales temas que emerge es el que concierne a la privacidad y a la protección de datos personales. Con la introducción de los sistemas informáticos en prácticamente todos los sectores sociales, nuestros datos son cada vez más útiles pero vulnerables a la vez, siendo colectados, tratados y transferidos, muchas veces, de una manera desprovista de preocupaciones éticas. Teniendo esto en cuenta, el presente estudio busca provocar una reflexión acerca de los impactos de la sociedad de la información, la comprensión y el alcance da la Inteligencia Artificial (IA), y, sobre todo, alentar la discusión sobre la importancia de considerar los valores éticos y morales en conjunto con el desarrollo tecnológico, intentando poner sobre la mesa los riesgos de que la tecnología, sin la observación de los principios éticos, pueda crear nuevos problemas sociales.&#x0D;