Artificial Intelligence Networks Research Articles

Antecedents and Outcomes of Marketing Agility: A Dynamic Capability Leading to Performance

In this digital era, digital startups require different capabilities to enhance performance. Marketing agility can be one of these capabilities, but how it can be developed remains unexplored. Therefore, this research aimed to investigate the antecedents and outcomes of marketing agility. It has investigated the role of artificial intelligence adoption and networking capability in developing marketing agility. Further, it examined the relationship between marketing agility and market performance. The data were gathered from FinTech managers, and Smart Pls 4 was used for data analysis. The results reported that artificial intelligence adoption and networking capability influence marketing agility, ultimately leading to superior market performance. The findings can act as a guideline for managers of FinTechs aiming to develop marketing agility or enhance performance in the market.

Retraction Note: Applications of artificial intelligence and hybrid neural network methods with new bonding method to prevent electroshock risk and insulation faults in high-voltage underground cable lines

Retraction Note: Applications of artificial intelligence and hybrid neural network methods with new bonding method to prevent electroshock risk and insulation faults in high-voltage underground cable lines

Artificial intelligence applications for enhancing organizational excellence: Modifying role of supply chain agility

The study’s goal was to demonstrate the modifying role of supply chain agility in the impact of artificial intelligence applications on organizational excellence in Jordanian e-commerce companies. The analytical and descriptive approach was adopted. The study population consisted of 160 companies operating in the e-commerce sector in Jordan. The sample comprised 400 respondents working in senior and middle management positions. The questionnaire was utilized to collect the data. The results showed an impact of artificial intelligence applications in all dimensions (expert systems and neural networks) on the organizational excellence of e-commerce companies in Jordan. The value of the adjusted coefficient of determination (Adj. R2) was .265%. Based on the model’s F value (4.1190) and its level of significance (P; 0.00), the impact of these techniques on organizational excellence is statistically significant. Additionally, the results displayed an impact of supply chain agility on improving the impact of artificial intelligence applications on organizational excellence. The value of the degree of influence ß after introducing the modified variable supply chain agility and the value of R Square increased by .11 at the significance level (Sig). = 0.000. This study recommended training workers to stay up to date with developments in artificial intelligence, expert systems, and neural networks in their operations, control of searching for this evidence within databases, and knowledge representation.

Overview, Concepts, Viewpoints to the Possibility of Legal Regulation of Artificial Intelligence Technology

The purpose of the study is to disclose the concept of "artificial intelligence", to outline the possibilities of legal regulation of AI. The article considers the studies of various authors, among which it is necessary to name P.M. Morhat, A. Romaglio, V.L. Entin. The scientific novelty of the work lies in the fact that the category of "artificial intelligence" is considered from the position of the legislation of the Russian Federation, as well as based on the opinion of domestic and foreign scientists. As a result, based on the comparative analysis of information contained in the works of various authors, regulatory legal acts of the Russian Federation, foreign judicial practice, it is concluded that representatives of the legal and scientific communities demonstrate the lack of consensus on the question of whether the units of artificial intelligence and neural networks can be subjects of copyright. A. Ramalho is convinced that AI units cannot be subjects of copyright, which is confirmed by the analysis of the following court decisions: Decision of the Court of Appeal of the Federal Court of Australia dated 13.04.2022, Decision of Shenzhen Nanshan District People's Court dated 24 December 2019. The opposing view is reflected in the Japanese "Intellectual Property Strategy Program 2016", which uses the following argument: it seems impossible to distinguish the product of AI units from human intellectual products. It is impossible to agree with this, as the images generated by neural networks often cause the "uncanny valley" effect in people; no neural network has been able to pass the Turing test so far.

Retraction Note: A comparative study on parameters of leaf-shaped patch antenna using hybrid artificial intelligence network models

Retraction Note: A comparative study on parameters of leaf-shaped patch antenna using hybrid artificial intelligence network models

Pruning techniques for artificial intelligence networks: a deeper look at their engineering design and bias: the first review of its kind

Pruning techniques for artificial intelligence networks: a deeper look at their engineering design and bias: the first review of its kind

Possibilities of Practical Use of Artificial Intelligence in Marketing Processes

This article discusses the possibilities of practical use of artificial intelligence in marketing processes. It is unambiguous, that marketing has entered a new era of development (marketing control) and day by day, the scale of changes brought by digital transformation becomes visible to the field of marketing, in terms of challenges and changes. No one can definitively say whether human creativity will be replaced by artificial intelligence, especially since artificial intelligence, in terms of content generation, is actively used by leading companies. For instance, it is significant that very recently, literally in front of our eyes, on March 14, 2023, the company OpenAI announced the launch of the 4th generation GPT-4 (Generative Pre-trained Transformer 4) in its artificial intelligence network. According to the company “The latest version of GPT-4 is 50 times smarter than its predecessor. GPT-4 Artificial intelligence will quickly become the future of advertising.” Advertisers are already actively using AI for audience identification and segmentation, for producing creative advertising content and for checking the effectiveness of advertising. Moreover AI assists with correct and improved quality of advertising performance, content details, as well as optimized time and costs - which is the most important in modern marketing and for its processes. Artificial intelligence will carry out advertising communication based on automatically processed analysis, in real time, at any scale. AI will automatically create advertisements, identify the target groups needed for its brand and will carry out testing ads on selected audiences, based on the received feedback, it will improve and strengthen the effectiveness of advertising, penetration and impact of advertising on the target audience. Keywords: Artificial intelligence, Content marketing, Generative Pre-trained Transformer, Google Marketing Platform.

Predicting Blood Glucose Levels with Organic Neuromorphic Micro-Networks.

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low-power hardware. Organic polymer-based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software-based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in-body computation of glucose prediction is investigated.

Analyzing the Possibilities of Implementation of AI and Social Networks in Teaching Foreign Language Students: Ukrainian Universities Case Study

The growing integration of artificial intelligence (AI) and social networks into the educational landscape has raised questions about their impact on teaching and learning, particularly in foreign language instruction. This study explores the didactic properties of social networks and AI as information and communication technology (ICT) tools in the context of foreign language education at the university level. Through a comprehensive literature review, the researchers analyzed global studies on the integration of AI and social networks in language learning, as well as research specific to the Ukrainian higher education context. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was conducted to systematically evaluate the potential benefits and challenges associated with the implementation of these technologies in foreign language instruction. The findings indicate that using AI-driven language learning applications, and social media platforms can significantly enhance vocabulary retention, language proficiency, and student engagement. However, the study also highlights potential threats, such as risks to academic integrity, entrenchment of discrimination, and the democratization of plagiarism. The research further emphasizes the importance of addressing teachers’ skills in navigating the ethical implications of AI integration in educational settings. The study provides valuable insights for university administrators, language instructors, and educational technology researchers. It underscores the need for a nuanced and informed approach to incorporating AI and social networks into foreign language curricula, balancing the pedagogical benefits with appropriate safeguards to ensure the quality and integrity of the educational process. The findings contribute to the evolving discourse on the role of emerging technologies in transforming higher education and language teaching.

Predicting hand grip force based on muscle electromyographic activity using artificial intelligence and neural networks

The research aims to find predictive values for hand grip strength based on electromyographic activity, in addition to identifying differences between measured grip strength and the predicted grip strength. The research sample included 12 advanced handball players, with their medical records verified. Researchers measured grip strength using a device designed to read Newton force, recording data in real-time with a sampling window of 0.1 seconds. This measurement was synchronized with the recording of muscle electromyographic activity (sEMG) using the Noraxon myoMOTION technique, with a frequency and number of channels set at 400Hz and 8 channels, respectively. The recommended methodology and conditions were strictly adhered to, with the process repeated for each player with complete rest intervals. The following research variables were adopted: peak electromyographic activity, root mean square, time to peak, time ratio between peak and minimum values, average peaks, area under the curve, peak sustain time, peak changes, and voluntary maximum contraction. Grip strength measurements using the designed device were conducted at three stages (50%, 75%, 100%), maintaining the specified intensity for 3 seconds. After data collection, preliminary processing involved isolation and purification to identify the most influential factors. IBM Statistical was the chosen technique for implementing neural networks and using artificial intelligence techniques to process data with a database synchronized using Python. The results generally supported some of the proposed ideas, with interesting findings revealing statistically insignificant and slight differences between recorded and expected grip strength

Application of geospatial multi-agent system for simulation of different aspects of tuberculosis transmission

BACKGROUND. The article presents the modeling of the epidemiological picture of tuberculosis (TB) using a multi-agent system. This is to provide an in-depth multi-agent modeling study with comprehensive strategies related to TB treatment and prevention.
 OBJECTIVE. To develop an algorithm that uses the capabilities of artificial intelligence and neural networks to build a geospatial model of TB transmission.
 MATERIALS AND METHODS. This study involves the development of an algorithm that uses the potential of artificial intelligence to create a geospatial model that highlights the different routes of TB transmission. The simulation process itself is characterized by a number of key stages, including initialization of the city, calibration of health parameters, simulation of the working day, spread of infection, evolution of disease trajectories, rigorous statistical calculations and transition to the next day. A comprehensive description of the course of active TB according to official data of the World Health Organization is given. Each agent is provided with an appropriate and consistent model that includes relevant health attributes and necessary rules for their dynamic evolution.
 RESULTS AND DISCUSSION. The process of modeling the transmission of TB in the urban landscape has been thoroughly investigated. Attributes and functional dependencies are divided into three different groups: static (or deterministic), stochastic, and empirical. Empirical parameters that determine the contagiousness of TB patients are subjected to careful analysis, during which the dynamic nature of their actual contagiousness is carefully compared. Therefore, the empirical parameters that determine infectivity are carefully established, which additionally allows for the quantitative determination of the level of TB infection. A complex simulation is presented that illustrates the spread of TB in a completely healthy environment devoid of any preventive or therapeutic measures. This simulation serves as a crucial study of the spread of the disease. To test the adequacy of the model and its sensitivity to the main parameters that determine the course of TB, a series of experiments using three different approximations, namely: a basic model, a model that includes mortality factors and a comprehensive model covering all relevant aspects, was conducted. Statistics used in this context include the number of infected and latent persons, recoveries and deaths. The article presents a comparative analysis between statistical information and a model that offers a comprehensive picture of the state of health of each person. The model allows careful monitoring of various factors, such as interpersonal contacts, place of residence, workplace, modes of transport used to move from place to place, and other vital aspects of a person’s life, together contributing to a more complete understanding of the dynamics of the disease.
 CONCLUSIONS. The calculations show the stability of the results and the absence of large fluctuations. The obtained statistical values of infected, latent and recovered people correlate with known medical data, and therefore, confirm the adequacy of the proposed model. This model allows tracking and analyzing the life and behavior of each agent, which allows for a detailed assessment and analysis of the spread of TB, as well as to develop a strategy to prevent the spread of this infection.

Transitional art forms: genesis and modifications

The article examines the processes of formation of transitional kinds of culture and highlights the grounds for their functioning. It is shown that the reasons for the emergence of transitional forms of culture also underlie cultural genesis, where pure examples of genres, styles, and trends are rather the exception to the rule. More often, the influence of other trends is always felt in the work, as V.D. Konen showed in her time in her work “The Third Layer”. At the same time, the author demonstrates those conditions of our time that contribute to an increase in the number of examples of transitional forms in music and culture in general, i.e. the development of the techno-environment and communication means, the capabilities of artificial intelligence and neural networks for simulating styles and genres both of classical elite and traditional culture, and mass entertainment, and social transformations leading to the formation of small social groups with their own aesthetic preferences. The article is of interest within the framework of the disciplines “Music Theory” and “Culture Theory”.

Application of Artificial Intelligence for Monitoring Shoreline Changes in the Central Coast of Vietnam

The identification and monitoring of coastline and shoreline plays an important role in coastal erosion assessment. The deep learning models can be a potential tool to detect the coastlines and shorelines in Vietnam using ultra-high resolution satellite images. The aims of the study are: i) To propose a set of indicators to determine the coastlines and shoreline; ii) To build deep machine learning models that automatically interpret the coastlines and shorelines on ultra-high resolution remote sensing images; and iii) To apply developed deep learning (DL) models to monitor coastal erosion in central Vietnam. Eight DL models were implemented based on four artificial intelligence network structures, including U-Net, U2-Net, U-Net3+, and DexiNed. Satellite images collected through Google Earth Pro software were used as input for all models. As a result, the U-Net model has been effectively applied to coasts in Cu De, Lai Giang, and Bien Lo estuaries,. The output results were used to calculate the rate of erosion/accretion in these areas. Additionally, the study indicated that coastline is a suitable criterion in assessing coastal erosion under the impact of sea level rise during storms. On the other hand, shoreline is a suitable criterion in assessing tidal fluctuations or instantaneous movements of wave currents during the year.
 

Can three-dimensional nitrate structure be reconstructed from surface information with artificial intelligence? — A proof-of-concept study

Nitrate is one of the essential variables in the ocean that is a primary control of the upper ocean pelagic ecosystem. Its three-dimensional (3D) structure is vital for understanding the dynamic and ecosystem. Although several gridded nitrate products exist, the possibility of reconstructing the 3D structure of nitrate from surface data has never been exploited. In this study, we employed two advanced artificial intelligence (AI) networks, U-net and Earthformer, to reconstruct nitrate concentration in the Indian Ocean from surface data. Simulation from an ecosystem model was utilized as the labeling data to train and test the AI networks, with wind vectors, wind stress, sea surface temperature, sea surface chlorophyll-a, solar radiation, and precipitation as the input. We compared the performance of two networks and different pre-processing methods. With the input features decomposed into climatology and anomaly components, the Earthformer achieved optimal reconstruction results with a lower normalized mean square error (NRMSE = 0.1591), spatially and temporally, outperforming U-net (NRMSE = 0.2007) and the climatology prediction (NRMSE = 0.2089). Furthermore, Earthformer was more capable of identifying interannual nitrate anomalies. With a network interpretation technique, we quantified the spatio-temporal importance of every input feature in the best case (Earthformer with decomposed inputs). The influence of different input features on nitrate concentration in the adjacent Java Sea exhibited seasonal variation, stronger than the interannual one. The feature importance highlighted the role of dynamic factors, particularly the wind, matching our understanding of the dynamic controls of the ecosystem. Our reconstruction and network interpretation technique can be extended to other ecosystem variables, providing new possibilities in studies of marine environment and ecology from an AI perspective.

Artificial intelligence based-prediction of energy efficiency and tailpipe emissions of soybean methyl ester fuelled CI engine under variable compression ratios

Biodiesel, a widely adopted substitute for conventional mineral diesel, is often derived from raw vegetable oils. The use of non-edible oils for the production of biodiesel has been favored for an extended period of time due to the potential impact on the availability of edible oil as a food source. The generation of soybean methyl ester biodiesel using the transesterification process. The primary objective of this study is to examine the potential use of soybean methyl ester biodiesel (referred to as "S") as a substitute for conventional diesel fuel in a standard light-duty compression ignition four-stroke, single-cylinder, water-cooled engine. The present study aimed to evaluate the impact of increased compression ratio (ranging from 14 to 18) on several combustion, performance, and emissions characteristics. This investigation was conducted using biodiesel blends consisting of 5%, 10%, 20%, and 40% soybean methyl ester, and the results were compared with those obtained using conventional diesel fuel. Additionally, in order to enhance the test results, a training process is being conducted on an artificial intelligence network (ANN). The analysis of the data indicated a positive correlation between the compression ratio (CR) and the in-cylinder pressure values at maximum load conditions. Additionally, a negative relationship was seen between the CR and the brake-specific fuel consumption (BSFC), suggesting a drop in BSFC as the CR rose. Upon evaluating the discharge numbers, it was seen that there was a notable reduction in other pollutants, a fall in NOx emissions, and an elevation in haze levels. Nevertheless, surpassing a sulphur content of 20% has an adverse impact on both emissions and the overall functioning of the engine. Based on the findings of the artificial neural network (ANN), the prediction results demonstrate a commendable capacity to accurately predict the experimental data with the R-value changing from 0.81919 to 0.95028 for all engine performance, combustion, and emission metrics.

A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors

Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are potentially applicable in artificial and spiking neural networks, memory arrays, and many other devices and circuits for artificial intelligence. In this paper, a complete electronic realization of an analog circuit model of the modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in the LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an important step towards the forthcoming design of complex memristor-based neural networks for artificial intelligence, for implementation in very high-density integrated circuits and chips.

Overcoming Uncertainty in Novel Technologies: The Role of Venture Capital Syndication Networks in Artificial Intelligence (AI) Startup Investments in Korea and Japan

This paper investigates how historical inter-firm syndication networks influence venture capitalists’ (VCs) propensity to invest in startups pursuing novel, uncertain technologies, with a focus on artificial intelligence (AI). We theorize that VCs’ positional attributes within cumulative syndication networks determine their access to external expertise and intelligence that aid AI investment decisions amidst informational opacity. Specifically, reachability to prior AI investors provides referrals and insights transmitted across short network paths to reduce ambiguity. Additionally, VC brokerage between disconnected industry clusters furnishes expansive, non-redundant information that is pivotal for discovering and assessing AI opportunities. Through hypotheses grounded in social network theory, we posit network-based mechanisms that equip VCs to navigate uncertainty when engaging with ambiguous innovations like AI. We test our framework, utilizing comprehensive historical records of global venture capital investments. Analyzing the location information of VC firms in this database, we uncovered a history of 14,751 investments made by Korean and Japanese firms. Using these data, we assembled an imbalanced panel dataset from 1984 to 2022 spanning 230 Korean and 413 Japanese VCs, with 4508 firm-year observations. Negative binomial regression analysis of this dataset reveals how historical relational patterns among venture capital firms foster readiness to evaluate unfamiliar innovations.

A performance comparison of the meta model methods for discharge coefficient prediction of labyrinth weirs

Weirs are hydraulic structures with various applications, such as flow measurement, flow diverting, and/or flow control. Labyrinth weir as a nonlinear weir with a folded crest enhances the spillway system discharge capacity by increasing the crest length for a given channel width. As a remarkable parameter, the discharge coefficient plays an important role in determining weirs' passing capacity. So, it is important that the discharge coefficient be accurately represented to ensure an appropriate and economical design. This research evaluates the prediction accuracy of four soft computing techniques, such as support vector machines (SVM), gene expression programming (GEP), software (QNET), and artificial intelligence networks (ANN), to predict the discharge coefficient of labyrinth weirs using three different scenarios. The model's input parameters included the total water head ratio (HT/P), magnification (Lc/W), and cycle wall angle (α). On reviewing the outcomes of the developed models, it appeared that each of them demonstrated satisfactory performance in predicting the discharge coefficient. However, ANN and SVM reveal more accuracy compared to other techniques. The prediction results reveal that the ANN as a superior model has the following performance metrics: R2 = 0.998, DC coefficient = 0.996, and RMSE = 0.006. The sensitivity analysis results indicate that HT/P is the most effective factor in determining the labyrinth weir discharge coefficient in all methods. This suggests that the ratio of HT (total water head) to P (weir height) has a significant impact on the overall model performance. Upon comparing the results of this research with those of other researchers, it is evident that the evaluation indicators for all methods employed in this study are relatively superior.

Macarius: HTR modelis senoms slaviškoms spausdintoms knygoms iš Rumunijos

The paper describes the process of creating and evaluating the HTR (Handwritten Text Recognition) model for Romanian Slavonic early printed books (first half of the 16th century, Middle Bulgarian Church Slavonic, Cyrillic Script) using the Transkribus software platform, based on the principles of artificial intelligence, machine learning and advanced neural networks. The HTR model was created on the material of Romanian Slavonic early printed books from Târgovişte printing house: the Liturgikon from 1508 and the Teatraevangelion from 1512 from the oldest printing house managed by hieromonk Macarius, as well as the Apostle from 1547 from the printing house managed by Dimitrije Ljubavić. The most important result of the paper is the creation of the first version of the generic HTR model Macarius (named in honour of hieromonk Makarije, the first South Slavonic and Romanian printer) with exceptional performance – the percentage of incorrectly recognized characters (including accent marks) is only 2.7%. Research has shown that this HTR model can also be used for the automatic recognition of Romanian Slavonic early printed books published in the second half of the 16th century. HTR model Macarius together with Ground Truth data is available to all users of the Transkribus platform, which ensures its wider use, as well as the possibility for further improvement of its performance.

STUDY OF LOGICAL OPERATIONS IN VECTORS OF A CIRCUIT OF HYPERCUBE

Parallel structure and switching circuit have become a fundamental theme in the concern of Artificial Intelligence and interconnection network and also it is revealed to be critical when researching in high performance. This Study helps to the process of breaking down larger process into normal form and independent secondary module, which helps to perform operations with various properties such as atomicity, consistency; isolation and switching features. They also help for manage and tune processing during faults in architecture, the results of which are combined upon completion as part of an overall algorithm. In this paper firstly we select the independent small module of hypercube then represent into data structure and apply logical operations into vectors