Field Of Intelligence Research Articles

Unveiling just-in-time decision support system using social media analytics: a case study on reverse logistics resource recycling

PurposeIn recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).Design/methodology/approachLeveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.FindingsTo substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.Originality/valueThis scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

Enhanced Linear and Vision Transformer-Based Architectures for Time Series Forecasting

Time series forecasting has been a challenging area in the field of Artificial Intelligence. Various approaches such as linear neural networks, recurrent linear neural networks, Convolutional Neural Networks, and recently transformers have been attempted for the time series forecasting domain. Although transformer-based architectures have been outstanding in the Natural Language Processing domain, especially in autoregressive language modeling, the initial attempts to use transformers in the time series arena have met mixed success. A recent important work indicating simple linear networks outperform transformer-based designs. We investigate this paradox in detail comparing the linear neural network- and transformer-based designs, providing insights into why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear neural network-based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on a majority of the popular benchmarks.

Intelligent robotic control system based on computer vision technology

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

Unveiling AI-Generated Financial Text: A Computational Approach Using Natural Language Processing and Generative Artificial Intelligence

This study is an in-depth exploration of the nascent field of Natural Language Processing (NLP) and generative Artificial Intelligence (AI), and it concentrates on the vital task of distinguishing between human-generated text and content that has been produced by AI models. Particularly, this research pioneers the identification of financial text derived from AI models such as ChatGPT and paraphrasing tools like QuillBot. While our primary focus is on financial content, we have also pinpointed texts generated by paragraph rewriting tools and utilized ChatGPT for various contexts this multiclass identification was missing in previous studies. In this paper, we use a comprehensive feature extraction methodology that combines TF–IDF with Word2Vec, along with individual feature extraction methods. Importantly, combining a Random Forest model with Word2Vec results in impressive outcomes. Moreover, this study investigates the significance of the window size parameters in the Word2Vec approach, revealing that a window size of one produces outstanding scores across various metrics, including accuracy, precision, recall and the F1 measure, all reaching a notable value of 0.74. In addition to this, our developed model performs well in classification, attaining AUC values of 0.94 for the ‘GPT’ class; 0.77 for the ‘Quil’ class; and 0.89 for the ‘Real’ class. We also achieved an accuracy of 0.72, precision of 0.71, recall of 0.72, and F1 of 0.71 for our extended prepared dataset. This study contributes significantly to the evolving landscape of AI text identification, providing valuable insights and promising directions for future research.

Exploring Antibiotic Resistance Through Artificial Intelligence: A Novel Perspective

Microbial resistance has long been linked to antibiotic resistance, a serious worldwide health issue. But as technology advances quickly in this day and age, a related phenomenon in the field of artificial intelligence [AI] is beginning to take shape. In order to better understand the idea of "antibiotic resistance" in the context of artificial intelligence [AI], this study will compare and contrast the evolution of bacterial resistance with potential obstacles in the design and implementation of intelligent systems. The increasing prevalence of AI systems across several industries highlights the striking similarities between their capacity to adapt and withstand hostile attacks and changing surroundings, and the biological resistance mechanisms seen in bacteria. This study explores the causes behind AI resistance, looking at how data drift, adversarial manipulations, and changing user behavior might cause machine learning systems to lose their effectiveness over time. The paper also examines the ethical ramifications of AI resistance, addressing issues with biases, unforeseen outcomes, and the influence of intelligent systems on society that are resistant to change or intervention. The area of antibiotic stewardship in medicine serves as an inspiration for the paper's discussion of potential mitigation techniques for AI resistance. Through the identification of parallels between AI resistance and antibiotic resistance in bacteria, this study adds to a better comprehension of the difficulties pertaining to the long-term viability and efficiency of intelligent systems. Since AI will continue to be a major influence on the future, it is critical to address the problem of "antibiotic resistance" in this context in order to ensure that AI is developed responsibly and ethically.

Raman and Mid-Infrared Spectroscopy Coupled With Machine–Deep Learning for Adulterant Detection in Ground Turmeric

The intricate nature of the global food supply chain and the presence of regulations spanning multiple jurisdictions contribute to an increased likelihood of food adulteration. This underscores the need for effective monitoring methods to guarantee the safety and nutritional quality of our food. In this context, the application of infrared spectroscopy-based techniques emerges as an environmentally friendly, non-invasive, and waste-minimizing solution for authenticating food products. Infrared spectra serve as unique molecular fingerprints, offering a multidimensional representation of how chemical bonds in the material interact with infrared light. Chemometrics, which are primarily linear-based models, play a crucial role in extracting essential information from spectral data, enabling dimensionality reduction, classification, and predictive analysis. Recent progress in the field of big data science and artificial intelligence has brought forth machine learning and deep learning algorithms explicitly designed to uncover features from complex multidimensional data, encompassing both linear and nonlinear relationships. These advancements have the potential to enhance the detection of adulterants in food products. This study assesses the accuracy of various shallow machine learning models and a deep learning model based on a one-dimensional convolutional neural network (1D CNN). The evaluation is conducted using Raman and infrared spectral data obtained from ground turmeric samples that were deliberately adulterated with five distinct substances. The study highlights the improved classification accuracy achieved through the implementation of the 1D CNN model.

Peluang dan Tantangan Implementasi Nilai-Nilai Pancasila melalui Pemanfaatan Microsoft Copilot dalam Pembelajaran di Era Digital

Currently, technology is experiencing rapid development, especially in the field of artificial intelligence (AI). One of the famous AIs is Microsoft Copilot. This artificial intelligence can be used to implement Pancasila values in learning. This research uses a qualitative research method of the literature study type. The results of the study show that there are opportunities and challenges in using Microsoft Copilot in the implementation of Pancasila values during the learning process in the digital era.

The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis.

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review.

Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology.

The white paper on artificial intelligence as a source for the formation of European Union legislation in the field of artificial intelligence

The article analyzes the provisions of the White Paper on artificial intelligence as a source of the formation of European Union legislation in the field of artificial intelligence. It is noted that the study of legal regulation of artificial intelligence in the European Union will contribute to the formation of the domestic source base in the field of application of artificial intelligence. It has been analyzed that in scientific circles, the provisions of the White Book on artificial intelligence are highlighted by domestic scientists in a fragmentary manner, which makes it difficult to comprehensively perceive the content and meaning of the White Book as a whole. The article states that the legislation of Ukraine needs improvement, which consists in compliance with European standards in the field of artificial intelligence. It has been established that it is advisable to enshrine provisions on the use of artificial intelligence at the legislative level. The article examines the provisions of the European Data Strategy, which recognizes artificial intelligence as an important component of the data economy. It has been studied that the provisions of the European Data Strategy provide for a combination of technology, industry, digital infrastructure and regulatory framework. It was noted that the provisions of the Strategy regarding artificial intelligence were further developed in the White Book. The article specifies the concept of artificial intelligence in accordance with the provisions of the White Book. It is noted that the Provisions of the White Book are aimed at ensuring consultation of interested parties, collection of feedback as a basis for future decision-making processes. It has been proven that the White Paper can be considered a political document containing the goals and possible actions of the European Commission regarding the regulation of artificial intelligence and contains a list of political proposals, intentions and recommendations. It is noted that the White Book is a framework for future legislative proposals on the formation of a legal framework related to artificial intelligence in the European Union. The article defines the structure of the main provisions of the White Book and establishes the fundamental principles that determine the development and implementation of artificial intelligence. Attention is focused on chapter 5 of the White Paper, which is devoted to the «Ecosystem of Trust: Regulatory Framework for Artificial Intelligence», which is devoted to the key elements of the regulatory framework for artificial intelligence in Europe.

OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.

Freestanding 3D graphite foam prepared by compressed growth template for superior electro-photo thermal performance

As a promising three-dimensional (3D) graphene architecture, graphene foam has excellent structural, electrical and thermal properties. However, due to the growth template limitation of commercial nickel/copper foam, graphene foam prepared by chemical vapor deposition (CVD) has a fixed 3D framework architecture, which limits its performance and application scenarios. In this paper, independent 3D graphite foam (GF) with different thickness and longitudinal compressibility was prepared by CVD method, and it was confirmed that mechanical compression of nickel foam template before graphite film deposition had a positive effect on the electrothermal, photothermal and mechanical properties of GF. At a low driving voltage of 3 V, the heating temperature of compressed GF with a low density of 8.9 mg/cm3 and a high porosity of 99.59 % can reach nearly 600 °C, which is nearly 250 °C higher than that of uncompressed GF. The excellent electrothermal performance of compressed GF is mainly attributed to the high crystallinity and thicker thickness (150–350 nm) of the graphite layers that make up the 3D framework, thus resulting in higher electrical conductivity. Under 1 and 2 sun illumination, the temperature of compressed GF can reach 83 °C and 109 °C respectively, which is 4–7 °C higher than that of the original GF. Compressed GF has better electrothermal, photothermal and radiant heating properties than traditional graphene/graphite foam and 2D graphene/graphite film. It not only has great application potential in high-efficient and energy-saving high-temperature electric heaters and photothermal converters, but also can be used as conductive supports or fillers with better performance, and has broad application prospects in flexible electronics, household appliances, artificial intelligence, medical care and other fields.

Assessing the Moderating Role of Customer Orientation on the Impact of Business Intelligence Tools on Digital Marketing Strategy Optimization

This research examines the interplay between Business Intelligence tools and digital marketing strategy optimization, with a focus on the moderating role of customer orientation. Utilizing a quantitative research design, the study explores how BI tools enhance digital marketing strategies and how customer orientation amplifies this effect among 207 respondents from the Jordanian telecommunications sector, reflecting a diverse range of experiences and perceptions within the industry. Through Structural Equation Modeling - Partial Least Squares (SEM-PLS), based on a stratified sample of these employees, the results confirmed that BI tools positively influence digital marketing strategy optimization. Additionally, customer orientation was found to significantly moderate this relationship, highlighting the importance of aligning technological capabilities with a customer-centric approach. The findings contribute valuable insights to marketing and business intelligence fields, suggesting strategies for organizations to enhance their market competitiveness and customer satisfaction, underpinned by a substantial engagement rate from the targeted demographic.

Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis.

Hemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential but can be difficult. Machine learning (ML) is a field of artificial intelligence that is emerging within medicine for accurate prediction modeling. This systematic review aimed to identify and evaluate all ML models that predict blood transfusion in trauma. This systematic review was registered on the International Prospective register of Systematic Reviews (CRD4202237110). MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were systematically searched. Publications reporting an ML model that predicted blood transfusion in injured adult patients were included. Data extraction and risk of bias assessment were performed using validated frameworks. Data were synthesized narratively because of significant heterogeneity. Twenty-five ML models for blood transfusion prediction in trauma were identified. Models incorporated diverse predictors and varied ML methodologies. Predictive performance was variable, but eight models achieved excellent discrimination (area under the receiver operating characteristic curve, >0.9) and nine models achieved good discrimination (area under the receiver operating characteristic curve, >0.8) in internal validation. Only two models reported measures of calibration. Four models have been externally validated in prospective cohorts: the Bleeding Risk Index, Compensatory Reserve Index, the Marsden model, and the Mina model. All studies were considered at high risk of bias often because of retrospective data sets, small sample size, and lack of external validation. This review identified 25 ML models developed to predict blood transfusion requirement after injury. Seventeen ML models demonstrated good to excellent performance in silico, but only four models were externally validated. To date, ML models demonstrate the potential for early and individualized blood transfusion prediction, but further research is critically required to narrow the gap between ML model development and clinical application. Systematic Review Without Meta-analysis; Level IV.

Artificial Intelligence in Astronomical Optical Telescopes: Present Status and Future Perspectives

With new artificial intelligence (AI) technologies and application scenarios constantly emerging, AI technology has become widely used in astronomy and has promoted notable progress in related fields. A large number of papers have reviewed the application of AI technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status of and research hotspots in telescope intelligence from these papers. This paper combines the development history of AI technology and difficulties with critical telescope technologies, comprehensively introduces the development of and research hotspots in telescope intelligence, conducts a statistical analysis of various research directions in telescope intelligence, and defines the merits of these research directions. A variety of research directions are evaluated, and research trends in each type of telescope intelligence are indicated. Finally, according to the advantages of AI technology and trends in telescope development, potential future research hotspots in the field of telescope intelligence are given.

New Scientific Field for Modelling Complex Dynamical Systems: The Cybernetics Artificial Intelligence (CAI)

Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. Despite the optimism and enthusiasm surrounding AI, its future may turn out to be a “catastrophic Winter” for the whole world, depending on who controls its development. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence and the Wise Anthropocentric Revolution. The text proposes specific solutions for achieving these two goals. Furthermore, the importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values.

Ethical Considerations in the Development of AI-Powered Healthcare Assistants

Advances in the field of artificial intelligence (AI) have led to the development of increasingly sophisticated health assistants that can provide support in diagnosis, treatment and general health management. However, as with the use of new technologies in the healthcare context, ethical considerations play an important role in the design, development, and implementation of AI-based health assistants. In this paper, we investigate various ethical considerations associated with the development of AI-based healthcare assistants. We explore issues such as the privacy and security of patient data, transparency and accountability in decision making, and the social and psychological impact of reliance on technology in the healthcare context. We also discuss efforts that can be taken to address these ethical challenges, including the development of appropriate regulatory guidelines, ongoing monitoring of system performance, and education and training for health professionals and end users. By seriously considering ethical aspects in the development of AI-based healthcare assistants, we hope to ensure that this technology can provide maximum benefit to patients while maintaining the ethical and moral values that underlie good healthcare practices.

Developing Time Series Forecasting Models with Generative Large Language Models

Nowadays, Generative Large Language Models (GLLMs) have made a significant impact in the field of Artificial Intelligence (AI). One of the domains extensively explored for these models is their ability as generators of functional source code for software projects. Nevertheless, their potential as assistants to write the code needed to generate and model Machine Learning (ML) or Deep Learning (DL) architectures has not been fully explored to date. For this reason, this work focuses on evaluating the extent to which different tools based on GLLMs, such as ChatGPT or Copilot, are able to correctly define the source code necessary to generate viable predictive models. The use case defined is the forecasting of a time series that reports the indoor temperature of a greenhouse. The results indicate that, while it is possible to achieve good accuracy metrics with simple predictive models generated by GLLMs, the composition of predictive models with complex architectures using GLLMs is still far from improving the accuracy of predictive models generated by human data scientists.

Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists

The growing field of explainable Artificial Intelligence (xAI) has given rise to a multitude of techniques and methodologies, yet this expansion has created a growing gap between existing xAI approaches and their practical application. This poses a considerable obstacle for data scientists striving to identify the optimal xAI technique for their needs. To address this problem, our study presents a customized decision support framework to aid data scientists in choosing a suitable xAI approach for their use-case. Drawing from a literature survey and insights from interviews with five experienced data scientists, we introduce a decision tree based on the trade-offs inherent in various xAI approaches, guiding the selection between six commonly used xAI tools. Our work critically examines six prevalent ante-hoc and post-hoc xAI methods, assessing their applicability in real-world contexts through expert interviews. The aim is to equip data scientists and policymakers with the capacity to select xAI methods that not only demystify the decision-making process, but also enrich user understanding and interpretation, ultimately advancing the application of xAI in practical settings.

A market driven identification of current and emerging skills requiring an MBA degree: a topic modelling approach

ABSTRACT The emerging field of Labour Market Intelligence focuses on extracting real-time information for professional skills. Online job posting websites provide unique opportunities to gather such intelligence, useful for job seekers and academic administrators in charge of preparing the future labour force. We propose a probabilistic topic modelling approach, incorporating occupational ontologies, to extract skillsets required for jobs needing a Master of Business Administration (MBA) degree. Analysing LinkedIn job postings in Pennsylvania, USA, we identified 28 job functions and associated skillsets confirming known demands and revealing new ones not yet identified in government and private labour market reports. By aggregating these skills, we can align MBA curricula with market needs, offering a superior alternative to conventional surveys. Our results are comparable with other popular neural models. Our approach provides improved computational efficiency with simpler methods Additionally, this methodology can be applied globally in other fields with existing occupational ontologies.