Utilization of Artificial Intelligence-Enabled Technologies by Agripreneurs in Ondo State, Nigeria

The research investigated the adoption of artificial intelligence (AI) technol-ogies among agricultural entrepreneurs in Ondo state, Nigeria. A purposive sample of 120 participants involved in agriculture was selected for the study. Socioeconomic characteristics analysis revealed that the mean age of the re-spondents was 48.3 years. A majority (77%) of the respondents were male, and approximately 68% were married. Regarding education, 32.5% had completed secondary education, while 32.5% had tertiary education. The av-erage annual income was 1,166,800 naira, with a significant proportion (71.7%) identifying as Christians. The study found a significant association between respondents’ awareness levels and their adoption of AI-enabled technologies (χ2 = 7.714, p = 0.005). Based on these findings, it is recom-mended that extension officers receive training in the latest agricultural technologies, including those enabled by AI. Furthermore, the study suggests the introduction of easily accessible and user-friendly AI technologies to farmers to enhance their productivity and income with minimal or no cost implications.

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

With the advancement of retinal imaging,hyperreflective foci(HRF)on optical coherence tomography(OCT)images have gained significant attention as potential biological biomarkers for retinal neuroinflammation.However,these biomarkers,represented by HRF,present pose challenges in terms of localization,quantification,and require substantial time and resources.In recent years,the progress and utilization of artificial intelligence(AI)have provided powerful tools for the analysis of biological markers.AI technology enables use machine learning(ML),deep learning(DL)and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments.Based on ophthalmic images,AI has significant implications for early screening,diagnostic grading,treatment efficacy evaluation,treatment recommendations,and prognosis development in common ophthalmic diseases.Moreover,it will help reduce the reliance of the healthcare system on human labor,which has the potential to simplify and expedite clinical trials,enhance the reliability and professionalism of disease management,and improve the prediction of adverse events.This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration(AMD),diabetic macular edema(DME),retinal vein occlusion(RVO)and other retinal diseases and presents prospects for their utilization.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

Application of Artificial Intelligence Technology in Landscape Architecture Industry

With wide application prospects in landscape industry,artificial intelligence technology plays an important role in improving work efficiency,optimizing design,strengthening construction management,and achieving intelligent maintenance.With the continuous development of technology,the application of artificial intelligence in landscape architecture industry will become more in-depth and extensive,which can provid powerful support for the innovation and development of the industry.It is hoped that the modernization process of the landscape industry can be promoted through the analysis on the application and difficulties of artificial intelligence technology in the landscape industry.

Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods

With the increasing proportion of renewable energy in China’s energy structure,among which photovoltaic power generation is also developing rapidly.As the photovoltaic(PV)power output is highly unstable and subject to a variety of factors,it brings great challenges to the stable operation and dispatch of the power grid.Therefore,accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy.Currently,the short-term prediction of PV power has received extensive attention and research,but the accuracy and precision of the prediction have to be further improved.Therefore,this paper reviews the PV power prediction methods from five aspects:influencing factors,evaluation indexes,prediction status,difficulties and future trends.Then summarizes the current difficulties in prediction based on an in-depth analysis of the current research status of physical methods based on the classification ofmodel features,statistical methods,artificial intelligence methods,and combinedmethods of prediction.Finally,the development trend ofPVpower generation prediction technology and possible future research directions are envisioned.

Evaluating Public Sentiments during Uttarakhand Flood: An Artificial Intelligence Techniques

Users of social networks can readily express their thoughts on websites like Twitter(now X),Facebook,and Instagram.The volume of textual data flowing from users has greatly increased with the advent of social media in comparison to traditional media.For instance,using natural language processing(NLP)methods,social media can be leveraged to obtain crucial information on the present situation during disasters.In this work,tweets on the Uttarakhand flash flood are analyzed using a hybrid NLP model.This investigation employed sentiment analysis(SA)to determine the people’s expressed negative attitudes regarding the disaster.We apply a machine learning algorithm and evaluate the performance using the standard metrics,namely root mean square error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).Our random forest(RF)classifier outperforms comparable works with an accuracy of 98.10%.In order to gain a competitive edge,the study shows how Twitter(now X)data and machine learning(ML)techniques can analyze public discourse and sentiments regarding disasters.It does this by comparing positive and negative comments in order to develop strategies to deal with public sentiments on disasters.

Technological anxiety:Analysis of the impact of industrial intelligence on employment in China

Employment is the greatest livelihood.Whether the impact of industrial robotics technology materialized in machines on employment in the digital age is an“icing on the cake”or“adding fuel to the fire”needs further study.This study aims to analyze the impact of the installation and application of industrial robots on labor demand in the context of the Chinese economy.First,from the theoretical logic and the economic development law,this study gives the prior judgment and research hypothesis that industrial intelligence will increase jobs.Then,based on the panel data of 269 cities in China from 2006 to 2021,we use the two-way fixed effect model,dynamic threshold model,and two-stage intermediary effect model.The objective is to investigate the impact of industrial intelligence on enterprise labor demand and its path mechanism.Results show that the overall effect of industrial intelligence on the labor force with the installation density index of industrial robots as the proxy variable is the“creation effect”.In other words,advanced digital technology has created additional jobs,and the overall supply of employment in the labor market has increased.The conclusion is still valid after the endogeneity identification and robustness test.In addition,the positive effect has a nonlinear effect on the network scale.When the installation density of industrial robots exceeds a particular threshold value,the division of labor continues to deepen under the combined action of the production efficiency and compensation effects,which will cause enterprises to increase labor demand further.Further research showed that industrial intelligence can increase employment by promoting synergistic agglomeration and improving labor price distortions.This study concludes that in the digital China era,the introduction and installation of industrial robots by enterprises can affect the optimal allocation of the labor market.This phenomenon has essential experience and reference significance for guiding industrial digitalization and intelligent transformation and promoting the high-quality development of people’s livelihood.

Exploration and Practice on Artificial Intelligence Information Technology Intergration into Practical Teaching of Acupuncture and Massage in the Background of New Medicine

[Objectives]In order to fully leverage the distinctive features of the top-tier national professional discipline of acupuncture and massage.[Methods]According to the characteristics of the scientific attributes of acupuncture and massage,combined with the top-tier na-tional professional AI practical teaching platform and artificial intelligence-related information technology,together with the teaching team with rich teaching experience.[Results]Innovative reforms and practical explorations are being conducted to integrate AI information technology into acupuncture and massage practical teaching under the background of new medicine.[Conclusions]Through practical research,the quali-ty of the practical teaching of students in acupuncture and massage major has been improved,playing a leading and exemplary role in related majors across the country.

A Comparative Study of Artificial Intelligence and Translation Software in Chinese-English Translation:A Focus on Literary and Technical Texts

In recent years,the domain of machine translation has experienced remarkable growth,particularly with the emergence of neural machine translation,which has significantly enhanced both the accuracy and fluency of translation.At the same time,AI also showed its tremendous advancement,with its capabilities now extending to assisting users in a multitude of tasks,including translation,garnering attention across various sectors.In this paper,the author selects representative sentences from both literary and scientific texts,and translates them using two translation software and two AI tools for comparison.The results show that all four translation tools are very efficient and can help with simple translation tasks.However,the accuracy of terminology needs to be improved,and it is difficult to make adjustments based on the characteristics of the target language.It is worth mentioning that one of the advantages of AI is its interactivity,which allows it to modify the translation according to the translator’s needs.

A Review of Artificial Intelligence Applications in Contemporary Computer Network Technologies

Rapid advancement in science and technology has seen computer network technology being upgraded constantly, and computer technology, in particular, has been applied more and more extensively, which has brought convenience to people’s lives. The number of people using the internet around the globe has also increased significantly, exerting a profound influence on artificial intelligence. Further, the constant upgrading and development of artificial intelligence has led to the continuous innovation and improvement of computer technology. Countries around the world have also registered an increase in investment, paying more attention to artificial intelligence. Through an analysis of the current development situation and the existing applications of artificial intelligence, this paper explicates the role of artificial intelligence in the face of the unceasing expansion of computer network technology.

Research on Interdisciplinary Project-Based Teaching in Primary Schools Catalyzed by Digital Intelligence Empowerment

With the rapid development of information technology,digital intelligence empowerment has gradually become an important direction of educational innovation.This paper uses the case analysis method to explore in depth how digital intelligence empowerment and project-based teaching can promote the integration of primary school curricula.Taking the teaching of intelligent patrol cars as an example,this paper analyses the positive role of digital intelligence empowerment in improving teaching effectiveness and cultivating students’comprehensive ability.The research results show that digital intelligence empowerment not only enriches teaching resources but also optimizes the teaching process.Combined with project-based teaching methods,it can effectively improve students’interest in learning and performance.This study provides a useful reference and inspiration for the project-based teaching of curriculum integration in primary schools and has certain practical significance and theoretical value for promoting the process of educational informatization.

Artificial intelligence technology in rock mechanics and rock engineering

The scope and scale of rock engineering activities have witnessed continuous expansion,which makes the geological conditions of rock engineering increasingly complex,and there are more and more types of disasters occurring during the construction and operation processes.The uncertainty of engineering geological information and the unclear nature of rock mass failure and disaster mechanisms pose increasingly prominent challenges to the study of rock mechanics and engineering problems.The artificial intelligence technology develops driven by data and knowledge,especially the proposal of digital-twin technology and metaverse ideas.This has injected new innovative impetus for the development of rock mechanics and engineering intelligence,where data and knowledge have been greatly enriched and updated in recent years.This article proposes the construction idea of a rock mechanics and engineering artificial intelligence system based on the metaverse,including intelligent recognition of three-dimensional(3D)geological structures,intelligent recognition of 3D geostress,intelligent recognition of rock mechanical behavior,intelligent evaluation,monitoring and early warning of rock engineering disaster,intelligent design of rock engineering,and intelligent construction of rock engineering.Two typical engineering applications are used as case studies to illustrate the integrated method of applying this system to solve engineering problems with multiple tasks.

Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

Analysis of Technical Intelligence of Tumor Cell Therapy Based on Patent Map

Objective To analyze the technical information in the field of tumor cell therapy in China,and to provide reference for identifying technical trends and predicting technical opportunities.Methods Based on the patent data in the field of tumor cell therapy in China,the patent map method was used to construct a scientific technical information analysis model.Then,the technical status of new drug research and development in this field was explored to identify technical opportunities.Results and Conclusion Studies have found that China’s tumor immunotherapy is in the growth stage.The technical innovation regions are mainly distributed in the east and innovative entities are enterprises.Technology hotspots are concentrated in areas such as A61P35,C12N5,and the patented technical efficacy is high.Besides,the technical research and development opportunities identified are closely related to the background in this field.To better promote the development of the industry,enterprises and research institutions should strengthen cooperation in technological innovation Meanwhile,they should pay attention to technical efficacy analysis to identify more technical opportunities,thereby effectively guiding innovation strategic decisions.

Exploration of Industrial Internet Security Technology and Application from the Perspective of Generative Artificial Intelligence

In recent years,artificial intelligence technology has developed rapidly around the world is widely used in various fields,and plays an important role.The integration of industrial Internet security with new technologies such as big models and generative artificial intelligence has become a hot research issue.In this regard,this paper briefly analyzes the industrial Internet security technology and application from the perspective of generative artificial intelligence,hoping to provide some valuable reference and reference for readers.

Research and Application of AI-Based Interactive Exhibits in Wuhan Museum of Science and Technology

This article aims to explore the development and application of AI-based interactive exhibits in Wuhan Museum of Science and Technology.By utilizing computer vision,natural language processing,and machine learning technologies,an innovative exhibit development and application system is proposed.This system employs deep learning algorithms and data analysis methods to achieve real-time perception of visitor behavior and adaptive interaction.The development process involves designing user interfaces and interaction methods to effectively enhance visitor engagement and learning outcomes.Through evaluation and comparison in practical applications,the potential of this system in enhancing exhibit interaction,increasing visitor engagement,improving educational effectiveness,and expanding avenues for scientific knowledge dissemination are validated.

Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

Gallbladder carcinoma in the era of artificial intelligence: Early diagnosis for better treatment

Gallbladder carcinoma(GBC)is the most common malignant tumor of biliary tract,with poor prognosis due to its aggressive nature and limited therapeutic options.Early detection of GBC is a major challenge,with most GBCs being detected accidentally during cholecystectomy procedures for gallbladder stones.This letter comments on the recent article by Deqing et al in the World Journal of Gastrointestinal Oncology,which summarized the various current methods used in early diagnosis of GBC,including endoscopic ultrasound(EUS)examination of the gallbladder for high-risk GBC patients,and the use of EUS-guided elasto-graphy,contrast-enhanced EUS,trans-papillary biopsy,natural orifice translu-minal endoscopic surgery,magnifying endoscopy,choledochoscopy,and confocal laser endomicroscopy when necessary for early diagnosis of GBC.However,there is a need for novel methods for early GBC diagnosis,such as the use of artificial intelligence and non-coding RNA biomarkers for improved screening protocols.Additionally,the use of in vitro and animal models may provide critical insights for advancing early detection and treatment strategies of this aggressive tumor.