Intelligent Knowledge Research Articles

Examining the Mediating Effect of Organizational Commitment and Job Satisfaction between Project Manager Emotional Intelligence and Project Success

The main purpose of this study is to examine the impact of the Project Manager’s emotional Intelligence on HVAC project success in Pakistan settings along with the mediating effect of job satisfaction and organizational commitment to bring clarity to the assessment of the relationship between the Project Manager’s emotional intelligence and project success in Pakistan settings. A quantitative research strategy is applied to collect realistic data from 210 Project Managers (from all the entities involved in the execution phase of the project the important ones being Sub-Contractors, Equipment Distributors as well as Product Manufacturers) of the HVAC industry of Pakistan. The collected data was analyzed and examined by using Structured Equation Modeling through the Partial Least Square method. To analysis the collected data SPSS and Smart PLS software were used. The findings of this study showed that the Project Manager’s emotional intelligence is positively related to project success and suggest the most evident result that there is a healthy and positive effect of Job Satisfaction and organizational commitment towards the success of a project. These suggestions amplify the need of the upper management to understand the significance of the mediating roles and its willingness to take care of necessary steps for letting and giving room to the Intelligence of the Project Manager having a positive outcome on the project success. Originality: This is first study in project management in Pakistan settings and delivers an advanced knowledge of emotional intelligence along with its importance to Project Success

Erratum to: Knowledge and Data in Artificial Intelligence: A Duel or a Duo

Erratum to: Knowledge and Data in Artificial Intelligence: A Duel or a Duo

A novel fuzzy-logic controller-MDsUPQC topology for power quality improvement in multi-feeder distribution system

In the current situation, the significant usage of massive non-linear functioned power electronic loads has been increased in domestic and industrial applications. The main problem of power-quality (PQ) deterioration in both voltage quality and current quality from nominal values and also damaging the single/multi feeder distribution systems. The major contribution is to alleviate the PQ issues by employing novel multi-devices unified power-quality conditioner (MDsUPQC) topology in a multi-feeder distribution system. This MDsUPQC comprises multiple voltage source inverter (VSI) devices connected with a common direct current-link (DC-link) capacitor which is controlled by a proportional-integral (PI) controller. However, this controller has some technical issues that are not suited for the regulation of common DC voltage at the desired level because of improper selection of gain values. The contribution of this work is proposing an intelligent fuzzy-logic (FL) DC-link controller-driven MDsUPQC device which evidences the intelligent knowledge base for better regulation of PQ issues. The technique and performance of the suggested strategy for PQ improvement, load sharing between feeders, and simulation results are presented with comparative analysis utilizing the MATLAB/Simulink software tool.

Inaugural Editorial: Editorial about the Founding of Medinformatics

The convergence of biology and artificial intelligence represents a paradigm shift in scientific exploration. The integration of AI into the study of omics—genomics, proteomics, transcriptomics, metabolomics, and beyond—has revolutionized our ability to decipher the complexities of biological systems. From predictive modeling of gene structures to the elucidation of protein interactions, from the analysis of transcriptomic profiles to the exploration of epigenetic landscapes, AI has become an indispensable tool in the biologist's toolkit. In this journal that focuses on the integration of information and life medicine, we embark on countless journeys of artificial intelligence applications in biomedical fields, focusing on the application of omics analysis in analyzing biological processes or disease occurrence and development. In addition, this journal also focuses on the forefront of interdisciplinary research, with a particular focus on the knowledge and methods of artificial intelligence, mathematics, physics, and other disciplines to solve biomedical problems, enhancing our rational understanding of life and disease. As we founded this magazine, we invited researchers, scholars, and practitioners from different backgrounds to publish cutting-edge biomedical informatics research or join our editorial board. Let us explore the frontiers of knowledge, push the boundaries of innovation, and together, forge a brighter future for biomedical science.

Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.

Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare.

Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector. A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained. Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery. This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

Exploring Allied Health Professionals' Perceptions and Knowledge of Artificial Intelligence in Healthcare

Exploring Allied Health Professionals' Perceptions and Knowledge of Artificial Intelligence in Healthcare

Radiology Professionals’ Knowledge, Attitude, Perception and Awareness of Artificial Intelligence in Medical Radiology and Imaging Technology

Artificial intelligence (AI) has rapidly emerged as a transformative force in the field of medical radiology and imaging technology. The advent of artificial intelligence (AI) has introduced transformative changes across various fields, including healthcare. Within medical radiology and imaging technology, AI has shown tremendous potential in enhancing diagnostic accuracy, improving workflow efficiency, and ultimately optimizing patient care. Advanced algorithms and machine learning models are increasingly being integrated into imaging modalities to aid in the detection, classification, and quantification of pathologies, offering a powerful adjunct to traditional radiological practices.

Knowledge, Perceptions, and Practice of Artificial Intelligence among Medical Students and Doctors at King Faisal University, Saudi Arabia

Background and Aims: Artificial Intelligence is no longer a new concept for the medical profession but is still finding its way into the realm of healthcare. Saudi Arabia is attempting to make most of it, especially in major healthcare settings. Methods: A cross-sectional survey was conducted at King Faisal University using a self-administered questionnaire assessing sociodemographic characteristics as well as knowledge, perceptions, and practice of AI. Universal sampling was implemented to recruit all medical students in the clinical stage of MBBS (Bachelors of Medicine and Surgery) and all available doctors working at KFU. Descriptive analysis and a chi-squared test was applied for determining correlation of sociodemographic variables with outcome variables, i.e., knowledge, perceptions, and practices relating to AI. Results: A total of 159 participants participated in our study. Those who were being taught about AI had significantly more basic knowledge about it (96.9%) than those not being taught about it (81.1%). Knowledge about deep learning was significantly higher among men (43.9%) than women (16.1%). The majority of the participants strongly agreed (84.4%) that AI is essential in medical practice, and most doctors and students have applied AI in the past in the medical field, are willing to practice it in the future, and/or are considering how to include it in the medical curriculum. Conclusions: There is a need for rigorous research, education, and focused training of present and future doctors to help them learn the significance of, and acquire the skills to implement, AI as one component of modern technology

Assessment of knowledge, attitude, and practice of artificial intelligence among medical students in a tertiary care hospital

Background: Artificial intelligence (AI) is gaining prominence in the field of medicine with potential benefits in diagnosis, treatment, and medical education. However, the extent to which medical students are informed about AI, their attitudes toward the integration of AI with medicine, and their engagement in AI-related practices remains an essential aspect yet to be explored. Aims and Objectives: This study aims at assessing the knowledge, attitude, and practice of AI among medical students in a tertiary care hospital and understanding the perspectives of students toward AI as a teaching tool and in medical practice. Materials and Methods: This is a cross-sectional study using an online survey consisting of closedended questions under categories of demographical data, knowledge, attitude, practice, and discussion among medical students of a government medical college and hospital in Kallakurichi. Results: Among 150 participants of the study, 74.16% were found to have a basic knowledge of AI cumulatively. 49.33% of the participants reported that they expected a positive impact of AI in health care; 14.0% shared that AI could have a negative impact on existing health-care practices; and the remaining 36.67% were impartial toward the outcome of AI integration. 10.67% of participants were extremely confident in their AI skills in medical practice; 26% reported that they were somewhat confident; 45.33% believed that they could adapt if needed; and 18% were uncertain of it. Finally, 17.33% were accepting toward AI evaluating their clinical skills, 36.0% were in strong denial of AI-based evaluation, and 46.67% agreed that it could be useful in some specific cases. Conclusion: In conclusion, the study highlights the current level of knowledge and attitudes of medical students toward AI. Incorporating AI tools and knowledge into medical education may lead to more informed and positively inclined future health-care professionals who can harness the potential benefits of AI in health care and advance medical knowledge.

Assessment of the level of knowledge on artificial intelligence in a sample of university professors: A descriptive study

Introduction: The knowledge of artificial intelligence (AI) by university professors provides them with the ability to effectively integrate these innovative technological tools, resulting in a significant improvement in the quality of the teaching and learning process.Objective: To assess the level of knowledge about AI in a sample of Peruvian university professors.Methods: Quantitative study, non-experimental design and descriptive cross-sectional type. The sample consisted of 55 university professors of both sexes who were administered a questionnaire to assess their level of knowledge about AI, which had adequate metric properties.Results: The level of knowledge about AI was low for 41.8% of professors, regular for 40%, and high for 18.2%. This indicates that there is a significant gap in the knowledge of university professors about AI and its application in education, which could limit their ability to fully leverage AI tools and applications in the educational environment and could affect the quality and effectiveness of teaching. Likewise, it was determined that age and self-perception of digital competencies of professors were significantly associated with their level of knowledge about AI (p<0.05).Conclusions: Peruvian university professors are characterized by presenting a low level of knowledge about AI. Therefore, it is recommended to implement training and professional development programs focused on artificial intelligence, in order to update and improve their skills in this field

The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals.

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community's understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.

Ancient Chinese Poetry Collation Based on BERT

The rapid advancements in intelligent knowledge management technologies, exemplified by generative large language models, have yet to be fully explored and applied in the field of collation of ancient Chinese poetry. This study investigates the application of BERT-based pre-trained models, namely bert-base-chinese and SikuBERT, in the specialized task of ancient Chinese poetry collation. Focusing on the poetry of Li Bai, we employed a meticulously curated dataset to fine-tune these models, with the objective of enhancing their ability to identify and rectify errors in classical verse. Through a systematic approach to model adaptation, our research aimed to bridge the gap between generic language understanding and the nuanced complexities of ancient poetry.Results indicate that both models, after fine-tuning, exhibit substantial improvement in accurately addressing textual issues in the poetry. Specifically, SikuBERT, with its background in classical Chinese literature, achieved an impressive accuracy rate exceeding 40% post-fine-tuning, reflecting a marked increase from its base performance, thereby validating the significance of domain-specific training data. Meanwhile, bert-base-chinese also displayed notable enhancements, underscoring the models’ adaptability to specialized tasks. The investigation further emphasizes the potential for artificial intelligence to contribute to the precision and efficiency of ancient literature studies. We highlight future directions including refining fine-tuning methodologies, expanding the models’ capability to generalize across diverse poetic styles and periods, and integrating multi-modal data to deepen the understanding of historical context and authorial intent. This work underscores the transformative role of AI in the digital preservation and scholarly analysis of ancient poetry, paving the way for innovative approaches in the field of classical literature collation.

ИНТЕГРАЦИЯ НЕЙРОННЫХ СЕТЕЙ В УЧЕБНУЮ ПРАКТИКУ СРЕДНЕГО ПРОФЕССИОНАЛЬНОГО ОБРАЗОВАНИЯ

The article is devoted to the study of the integration of neural networks into the educational practice of secondary vocational education. The paper considers key areas of application of neural networks, including adaptive learning systems, intelligent knowledge assessment systems, virtual mentors and analysis of educational data. The study is aimed at assessing the impact of such technologies on the quality of education, as well as on the development of digital competencies in students. The main advantages and risks of using neural networks are identified, recommendations for the successful implementation of these technologies in the educational process are proposed.

The connections between Historic Urban Landscape layers in Jingdezhen, the porcelain capital of China

The Historic Urban Landscape (HUL) approach offers an innovative integrated approach to urban conservation based on layering analysis. However, adapting the HUL layering model to the East Asian context, particularly China, is problematic. This issue can be addressed by incorporating connection analysis into the HUL approach. The current study emphasises the need to consider the interwoven spatial structure of the urban landscape and treat deeper cultural-natural ties as those embodied in the traditional Chinese concept of Fengshui. Taking Jingdezhen, the historic centre of China’s traditional ceramics industry, as a case study, this study demonstrates local knowledge of natural geography and local intelligence regarding settlement location and design. The results indicate that the natural system is at the core of Jingdezhen’s HUL, and the elements of nature, function and spirit form a holistic and interactive system.

Design of Intelligent Customer Service Knowledge Base for Medical Insurance Based on Foundation-scale Models

In this paper, we propose a method for constructing an intelligent customer service knowledge base system for medical insurance based on natural language processing (NLP) models. This method utilizes a local vector database to construct the knowledge base system and retrieves industry knowledge by calling the vector database through foundation-scale models. The use of this technology ensures the security of business data and reduces platform training costs. The intelligent knowledge base of medical insurance constructed using this method can automatically handle user’s inquiries, complaints, and general business transactions, effectively improving the efficiency and intelligence level of customer service.

Intelligent Knowledge Based Algorithm for Internet Quality of Services Enhancement

The accumulated knowledge in the internet servers can be utilized for its performance enhancement. In this paper an intelligent knowledge-based algorithm (IKB) proposed for internet quality of services enhancement. The enhancement achieved by optimizing the bandwidth distribution in the network and adaptation of on demand bandwidth distribution concept. The internet customer behavior can be used to extract the set of indicators of the services and bandwidth demand. A MATLAB simulation program is written based on the physical and mathematical modelling taking in consideration the flow in and out of the user traffic. The results include the utilization concept and the Quality of Services improvement percentages.

Assessment of knowledge and awareness of artificial intelligence and its uses in dentistry among dental students in Jammu and Kashmir: A questionnaire based survey

Artificial intelligence (AI) is the ability of computers or other machines to secure understanding in order to carry out tasks that traditionally need human knowledge. The present study aims to assess their knowledge, attitude and practice towards artificial intelligence among dental students. A survey based questionnaire was done to study the knowledge, attitude and practice about artificial intelligence among dental students of Jammu & Kashmir. A total questionnaire of 15 questions were collected by Google form app. SPSS statistical analysis was done. A total of 540 participants participated in this survey. The results were collected and data were analyzed. Maximum of the participants had good knowledge, positive attitude and practical nature towards the artificial intelligence. The difference of the result is statistically significant (p<0.05) From the study it was concluded that the majority of participants were aware of artificial intelligence and its applications.

Research on prediction model of scaling in ASP flooding based on data mining

As a result of alkali ASP flooding in oil and gas fields, strata and pipelines become seriously scaled, which poses a threat to the normal operation of crude oil production. We propose an intelligent knowledge reasoning model for dynamic scaling prediction in order to address the problems of high directivity, poor generalization ability, and poor application effect of existing scaling prediction methods. The model framework includes the knowledge acquisition layer which mainly relates to the manual acquisition of scaling prediction knowledge and the intelligent training of the knowledge base, and it includes the knowledge modeling layer that provides a set of standard domain common ontology and knowledge organization system using the ontology modeling technology, it also includes the knowledge inference layer which is the application layer of the model. The three layers collaborate and finally complete the scaling prediction through inference and expression. A total of 238 wells were selected for experimentation in the northern development area of the Xingshugang Oilfield. Experimental results indicate that the model has the highest accuracy of 91.87%. Additionally, the time series prediction trend for the six ions matches the trend of change in ion concentration in the scaling state, verifying the accuracy of the model’s predictions.

PEMERKASAAN PENDAKWAH MUALAF DENGAN ILMU KECERDASAN SPIRITUAL DI MUIP

The study aims to identify the knowledge, learning experience, and needs of the module of spiritual intelligence training and modules among converted preachers appointed by the Majlis Ugama Islam dan Adat Resam Melayu Pahang (MUIP). A qualitative method using an interview instrument is used to collect data from the respondents. The interview instrument has been constructed based on the research objectives and has undergone a process of validation by experts. The respondents were randomly selected from a list of converted preachers who have registered and appointed by MUIP. They are from diverse ethnic backgrounds such as Orang Asli, Chinese, and Indians. The interview is conducted face-to-face and online, depending on the location and time factors of the respondent. All interview recordings are translated into text using the Inscribe application. Respondent answers are analysed and categorised by theme. The survey showed that 60% of respondents had spiritual intelligence knowledge at low and medium levels. Some respondents (50%) said they were exposed to the spiritual intelligence knowledge formally and some informally. The majority of respondents (80 %) said they needed the spiritual intelligence knowledge to strengthen themselves internally and consolidate their reputation. Therefore, there is a need to construct a module on spiritual intelligence knowledge and deliver as a formal education program to the converted preachers to strengthen the preaching efforts in Malaysia.