Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.

Artificial Intelligence-Empowered Resource Management for Future Wireless Communications: A Survey

How to explore and exploit the full potential of artificial intelligence(AI)technologies in future wireless communications such as beyond 5G(B5G)and 6G is an extremely hot inter-disciplinary research topic around the world.On the one hand,AI empowers intelligent resource management for wireless communications through powerful learning and automatic adaptation capabilities.On the other hand,embracing AI in wireless communication resource management calls for new network architecture and system models as well as standardized interfaces/protocols/data formats to facilitate the large-scale deployment of AI in future B5G/6G networks.This paper reviews the state-of-art AI-empowered resource management from the framework perspective down to the methodology perspective,not only considering the radio resource(e.g.,spectrum)management but also other types of resources such as computing and caching.We also discuss the challenges and opportunities for AI-based resource management to widely deploy AI in future wireless communication networks.

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide,with nearly 25% of adults suffering from one or more chronic health conditions,thus placing a heavy burden on individuals,families,and healthcare systems.With the advent of the“Smart Healthcare”era,a series of cutting-edge technologies has brought new experiences to the management of chronic diseases.Among them,smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state.However,how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management,in terms of quality of life,patient outcomes,and privacy protection,is an urgent issue that needs to be addressed.Artificial intelligence(AI)can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases.In addition,blockchain can improve healthcare services by authorizing decentralized data sharing,protecting the privacy of users,providing data empowerment,and ensuring the reliability of data management.Integrating AI,blockchain,and wearable technology could optimize the existing chronic disease management models,with a shift from a hospital-centered model to a patient-centered one.In this paper,we conceptually demonstrate a patient-centric technical framework based on AI,blockchain,and wearable technology and further explore the application of these integrated technologies in chronic disease management.Finally,the shortcomings of this new paradigm and future research directions are also discussed.

Artificial intelligence in perioperative management of major gastrointestinal surgeries

Artificial intelligence(AI)demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms.The purpose of this review was to summarize concepts,the scope,applications,and limitations in major gastrointestinal surgery.This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists,surgeons,and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management.AI uses available databases called“big data”to formulate an algorithm.Analysis of other data based on these algorithms can help in early diagnosis,accurate risk assessment,intraoperative management,automated drug delivery,predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment.Perioperative physicians,anesthesiologists,and surgeons are well-positioned to help integrate AI into modern surgical practice.We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context.Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care,and is the way forward in future perioperative management of major surgery.

Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living andsustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install ITplatforms to collect and examine massive quantities of data. At the same time,it is essential to design effective artificial intelligence (AI) based tools to handlehealthcare crisis situations in smart cities. To offer proficient services to peopleduring healthcare crisis time, the authorities need to look closer towardsthem. Sentiment analysis (SA) in social networking can provide valuableinformation regarding public opinion towards government actions. With thismotivation, this paper presents a new AI based SA tool for healthcare crisismanagement (AISA-HCM) in smart cities. The AISA-HCM technique aimsto determine the emotions of the people during the healthcare crisis time, suchas COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides,brain storm optimization (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antennasearch with extreme learning machine (BAS-ELM) method was utilized forclassifying the sentiments as to various classes. The use of BSO and BASalgorithms helps to effectively modify the parameters involved in the DBNand ELM models respectively. The performance validation of the AISA-HCMtechnique takes place using Twitter data and the outcomes are examinedwith respect to various measures. The experimental outcomes highlighted theenhanced performance of the AISA-HCM technique over the recent state ofart SA approaches with the maximum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94.

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

Subsurface analytics: Contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management

Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models.

Artificial Intelligence Technique and Wireless Sensor Networks in En­ergy Management System for Secure Power Optimization

The modern basic building blocks of a control system consist of data acquisition,dispensation of data by the system operators and the remote control of system devices.However,the physical controls,technical ex­aminations and deductions were originally implemented to aid the process and control of power system design.The complexity of the power system keeps increasing due the technical improvements,diversity and dynamic requirements.Artificial intelligence is the science of automating intelligent activities presently attainable by individuals.Intelligent system techniques may be of excessive benefit in the application of area power system con­trols.Whereas smart grid can be measured as a modern electric power grid structure for better productivity and dependability via automatic control,excessive power converters,modern communications setup,sensing and metering equipment,and modern energy management techniques estab­lished on the optimization of demand,energy and network accessibility,and so on.The enormous depiction of the entire transmission grid,in the perspective of smart grids,is quite unclear;and in Nigeria no studies have been put on ground in order for the existing network to be turn into a smart grid.In this research work emphasis is placed on generation and transmis­sion stations;power optimization using artificial intelligent techniques and wireless sensor networks for power control management system.

Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule‑based decision‑making model

A broad range of companies around the world has welcomed artificial intelligence(AI)technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis.This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies,which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment.To obtain this goal and inspired by a model ensemble,we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing,fuzzy set theory,and a multi-attribute decision making algorithm.The results display that the order of priority in improvement—(A)AI application strategy,(B)AI governance,(D)the human factor,and(C)data infrastructure and data quality—is based on the magnitude of their impact.This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.Highlights Artificial intelligence(AI)promotes the sustainability development of audit tasks.A fuzzy MRDM model extracts key factors from large amounts of data.Fuzzy decision-making trial and evaluation laboratory analysis accounts for dependence and feedback among factors.An effective framework of AI-driven business audit is proposed in which“AI cognition of senior executives”is the most important criterion.

The Use of Artificial Intelligence for Disasters

In recent years, due to an increasing number of extreme meteorological events, the urgent need to use artificial intelligence in their management has emerged. The ability to handle and prevent such events requires disaster management. Artificial intelligence is extensively used in forecasting and preparing for disasters, as well as for mitigating and minimizing damage and in the response phase to effectively help in better and more rapid responses to disasters. This paper examines to identify the uses of artificial intelligence technologies in reducing the impact of various disasters and seeks to investigate the possibility of linking artificial intelligence technologies based on information and communication technology and reducing the effects of disasters. We conclude the paper with the challenges facing artificial intelligence technologies.

Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

Smart Society and Artificial Intelligence:Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

The implementation of artificial intelligence(AI)in a smart society,in which the analysis of human habits is mandatory,requires automated data scheduling and analysis using smart applications,a smart infrastructure,smart systems,and a smart network.In this context,which is characterized by a large gap between training and operative processes,a dedicated method is required to manage and extract the massive amount of data and the related information mining.The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics(AD)for smart management,which is exploitable in any context of Society 5.0,thus reducing the risk factors at all management levels and ensuring quality and sustainability.We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes,for reducing training costs,for improving production yield,and for creating a human–machine cyberspace for smart infrastructure design.The results obtained in 12 international companies demonstrate a possible global standardization of operative processes,leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself.Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions,with the related smart maintenance and education.

Artificial intelligence for COVID-19 and future pandemics:A mini-review

The pandemic of severe acute respiratory syndrome coronavirus 2 has spread very quickly all over the world and has become an unparalleled public health crisis.This unforeseen and exceptional situation has instigated a wave of research to investigate the virus,track its spread,and study the disease it causes.Current methods of diagnosis and monitoring largely rely on polymerase chain reactions and enzyme-linked immunesorbent assay methods.In this hour of crisis,researchers are looking for new technologies to monitor and control such disease outbreaks.Artificial intelligence(AI)is one such technology.Being an evidence-based tool,this technology has the potential to upgrade our disease management strategies and help us to restrict the spread of such diseases.AI can play an effective role in tracking the spread of diseases,screening of the population,identifying patients and developing treatments of diseases.Through this review,we aim to analyze the role of AI in the diagnosis,monitoring and treatment of diseases like coronavirus disease 2019,with most recent updates and assess the prospects of this technology in the management of such diseases.

Expert recommendation on collection,storage,annotation,and management of data related to medical artificial intelligence

Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform criteria for medical data quality have yet to be established.Therefore,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical AI.This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

Information technology and artificial intelligence support in management experiences of the pediatric designated hospital during the COVID-19 epidemic in 2022 in Shanghai

Objective:To describe the information technology and artificial intelligence support in management experiences of the pediatric designated hospital in the wave of COVID-19 in Shanghai.Methods:We retrospectively concluded the management experiences at the largest pediatric designated hospital from March 1st to May 11th in 2022 in Shanghai.We summarized the application of Internet hospital,face recognition technology in outpatient department,critical illness warning system and remote consultation system in the ward and the structed electronic medical record in the inpatient system.We illustrated the role of the information system through the number and prognosis of patients treated.Results:The COVID-19 designated hospitals were built particularly for critical patients requiring high-level medical care,responded quickly and scientifically to prevent and control the epidemic situation.From March 1st to May 11th,2022,we received and treated 768 children confirmed by positive RT-PCR and treated at our center.In our management,we use Internet Information on the Internet Hospital,face recognition technology in outpatient department,critical illness warning system and remote consultation system in the ward,structed electronic medical record in the inpatient system.No deaths or nosocomial infections occurred.The number of offline outpatient visits dropped,from March to May 2022,146,106,48,379,57,686 respectively.But the outpatient volume on the internet hospital increased significantly(3,347 in March 2022 vs.372 in March 2021;4,465 in April 2022 vs.409 in April 2021;4,677 in May 2022 vs.538 in May 2021).Conclusions:Information technology and artificial intelligence has provided significant supports in the management.The system might optimize the admission screening process,increases the communication inside and outside the ward,achieves early detection and diagnosis,timely isolates patients,and timely treatment of various types of children.

Research on the application of artificial intelligence in the computer network technology

As the product of the mutual infiltration of the various disciplines such as the control theory, information theory, system theory, computer science, physiology, psychology, mathematics, philosophy and so on, the research field of the theory and application of artificial intelligence technology covers almost all the areas of human activity. In recent years, the rapid development of computer network technology produces and drives a batch of new scientific research fields. Among them, the application of artificial intelligence in the computer network technology is a hot topic which is academically and technically strong and can bring obvious economic benefit.

Dietary counseling based on artificial intelligence for patients with nonalcoholic fatty liver disease

BACKGROUND About 25%of the general population in Japan are reported to have nonalcoholic fatty liver disease(NAFLD).NAFLD and nonalcoholic steatohepatitis carry a risk of progressing further to hepatocellular carcinoma.The primary treatment for NAFLD is dietary therapy.Dietary counseling plays an essential role in dietary therapy.Although artificial intelligence(AI)-based nutrition management software applications have been developed and put into practical use in recent years,the majority focus on weight loss or muscle strengthening,and no software has been developed for patient use in clinical practice.AIM To examine whether effective dietary counseling is possible using AI-based nutrition management software.METHODS NAFLD patients who had been assessed using an AI-based nutrition management software application(Calomeal)that automatically analyzed images of meals photographed by patients and agreed to receive dietary counseling were given dietary counseling.Blood biochemistry tests were performed before(baseline)and 6 mo after(6M follow-up)dietary counseling.After the dietary counseling,the patients were asked to complete a questionnaire survey.RESULTS A total of 29 patients diagnosed with NAFLD between August 2020 and March 2022 were included.There were significant decreases in liver enzyme and triglyceride levels at the 6M follow-up compared to baseline.The food analysis capability of the AI used by Calomeal in this study was 75.1%.Patient satisfaction with the AIbased dietary counselling was high.CONCLUSION AI-based nutrition management appeared to raise awareness of dietary habits among NAFLD patients.However,it did not directly alleviate the burden of registered dietitians,and improvements are much anticipated.

Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

Leveraging Geospatial Technologies for Resource Optimization in Livestock Management

Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.