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 in tunnel construction: A comprehensive review ofhotspots and frontier topics

Application of Artificial Intelligence(AI)in tunnel construction has the potential to transform the industry by improving efficiency,safety,and cost-effectiveness.This paper presents a comprehensive literature review and analysis of hotspots and frontier topics in artificial intelligence-related research in tunnel construction.A total of 554 articles published between 2011 and 2023 were collected from the Web of Science(WOS)core collection database and analyzed using CiteSpace software.The analysis identified three main study areas:Tunnel Boring Machine(TBM)performance,construction optimization,and rock and soil mechanics.The review highlights the advancements made in each area,focusing on design and operation,performance prediction models,and fault detection in TBM performance;computer vision and image processing,neural network algorithms,and optimization and decision-making in construction optimization;and geo-properties and behaviours,tunnel stability and excavation,and risk assessment and safety management in rock and soil mechanics.The paper concludes by discussing future research directions,emphasizing the integration of AI with other advanced technologies,realtime decision-making systems,and the management of environmental impacts in tunnel construction.This comprehensive review provides valuable insights into the current state of AI research in tunnel engineering and serves as a reference for future studies in this rapidly evolvingfield.

Bridging the Gap:Integration of Artificial Intelligence with Organ-on-Chip(AI-OoC)

.Organ-on-Chip(OoC)has emerged as a revolutionary approach to emulate human organ function-ality in vitro,offering unparalleled insights into physiological processes and disease modeling.The integration of artificial intelligence(AI)with OoC platforms presents a transformative synergy,combining the precision of microscale organ replication with the analytical prowess of intelligent algorithms,is emerging as a transforma-tive force in harnessing the full potential of OoC.This perspective investigates the multifaceted implications of integrating AI with OoC,examining its impact on biomedical research,acknowledging the synergistic po-tential that arises from combining the precision of microscale organ replication with the analytical capabilities of intelligent algorithms,and fostering a future where the intricate workings of the technology and biology.

Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent world.When an outbreak occurs,each country must have a well-coordinated and preventative plan to address the situation.Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living.Although intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern society.Here,we used an artificial intelligence AI-based system for disease prediction using an Artificial Neural Network(ANN).The ANN improved the regularization of the classification model,hence increasing its accuracy.The unconstrained opti-mization model reduced the classifier’s cost function to obtain the lowest possible cost.To verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed models.The proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

生成式人工智能商业秘密保护困境及对策

生成式人工智能以大语言模型为基础,与传统的人工智能相比可以执行更加多样化的任务,其作为商业秘密进行保护具有保护范围广、保护成本低、保护期限更长等优势。但也存在缺乏商业秘密客体保护范围规定,合理保密措施认定标准不明确、例外规定不完善等困境。为了更好地发挥商业秘密保护的作用,需要进一步完善相关法律法规,明确商业秘密保护客体范围、合理保密措施认定标准和例外规定,更好地保护生成式人工智能权利人的商业秘密。

生成式人工智能在司法中的运用:前景、风险与规制

ChatGPT等生成式人工智能对社会生活的许多方面产生影响,带给人们巨大的冲击。在司法领域,生成式人工智能亦有广阔的运用前景,如可用于“示意证据”的生成、诉讼文书的制作等,从而带来裁判方式的变革。然而生成式人工智能在司法中的运用也会带来一些问题,如信息真实性方面的缺陷、裁判方式方面的挑战和数据安全方面的风险等。在此种情况下,应当从防范虚假信息、限制生成式人工智能裁判、保护数据安全等多个层面着手对生成式人工智能进行规制,实现其在司法中的合理运用。

AI绘画创作的运行规律及应用

从AI绘画发展现状入手,分析了基于生成式对抗网络进行深度学习的AI绘画基本运行训练原理,探讨了AI绘画面临的问题和AI绘画对于艺术相关行业的影响。

基于生成对抗网络(GANs)革新包装设计

深度学习模型生成对抗网络(GANs)可以在包装设计领域良好的应用,探讨如何使用生成对抗网络(GANs)来革新包装设计。阐述GANs的基本原理和工作方式,引入了一种基于GANs的新型包装设计方法,通过将GANs应用于包装设计的生成和选择中,可以实现更加创新、个性化的包装设计效果。通过GANs在包装设计中的应用,可以大幅提高设计的多样性、创意性以及用户体验。

Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

Soil salinity is a serious land degradation issue in agriculture.It is a major threat to agriculture productivity.Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation water.For the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)rate.The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)rate.ML-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity level.The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)determination.The validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm day-1.The applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.

A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

AI技术下影像创作研究

探讨了人工智能技术在影像创作领域中的多重应用,重点关注AI技术对影像创作过程的影响,详尽介绍了AIGC图像的核心概念,剖析其在图像生成、风格迁移及影像增强等方面的应用,探讨如何借助AI技术推动影像创作进展。

基于AI技术的花境设计应用分析

人工智能(AI)在各个领域都展现出巨大的潜能,如何利用AI技术辅助风景园林领域正处于初步摸索与完善阶段。通过计算机视觉和机器学习算法,训练一种自动化的花境设计模型,以探索AI技术在花境设计领域中的应用。以河南省首届花境大赛——北龙湖湿地公园花境展为案例素材库,收集了大量的花境作品照片作为模型训练数据,利用计算机视觉算法对花境实景图像进行分析和特征提取;使用机器学习算法训练模型,根据语义分割图和输入的关键词生成新的花境设计方案。机器学习模型可以为不同类型的花境场景生成高质量和多样化的设计方案,并且可以识别和提取一些花卉植物特征,如植物种类、尺度、空间关系等。此外,对AI生成的花境配置方案效果进行了评价,验证AI技术在花境设计应用中的可行性及适用性。旨在为AI技术在植物景观设计领域的理论研究及设计实践应用提供创新研究视角及思路。

人工智能发明成果对专利制度的挑战——以遗传编程为例

人工智能技术的发展给人类生活带来诸多机遇和挑战,在现有的知识产权法律框架下,以遗传编程为代表的人工智能技术自动生成的技术方案,对于传统的专利授权条件即新颖性、创造性和实用性的标准适用带来新问题。人工智能发明物能否获得专利法的保护,受到学术界与理论界的广泛关注。专利制度对科技与经济发展的激励效应促使人工智能技术的进步与创新。基于传统的"三性"要求,专利授权范围的界定不能"一刀切",其需结合人工智能的发展水平、贡献大小、普及程度、技术特征等具体情形不断调整,实现专利法律制度稳定性与灵活性的平衡。

基于人工智能的穿戴式颈椎病预防系统

伴随现代工作、生活方式的改变,颈椎病的发病率逐年上升。鉴于颈椎病的主要成因之一为头、颈部长期保持固定姿态,该研究团队研发了一套穿戴式颈椎病预防系统。系统主要包含基于加速度传感器的头颈部运动采集模块以及基于人工智能的头颈部运动识别模块。实验结果表明,系统可以准确识别头颈部姿态的长期保持,并在运动识别模块的监督下指导使用者完成有效的运动疗法。系统的使用有利于预防颈椎病的发病。

智能医学发展战略思考

随着我国科技创新与人工智能的快速发展,人工智能在医学中的应用越来越广,时代提出了智能医学的概念.本文通过文献分析,调研与咨询,从大数据、互联网、人工智能与智能医学、智慧医疗,以及学科建设、人才培养、相关法规制度、伦理建设等方面提出智能医学发展战略相关建议.

人工智能生成物的著作权法保护初探

根据人工智能创作过程对人的依赖程度,将人工智能生成物类型化为来自于人类的生成物(第一类生成物)和非来自于人类的生成物(第二类生成物)。从解释论的角度来看,第一类生成物可以受到现行著作权法的保护,而第二类生成物不满足构成作品的要件。从立法论的角度来看,应基于激励理论来考虑第二类生成物著作权法保护的必要性,但存在一些制度设计上的障碍。出于促进产业发展的政策考量,在人工智能技术成熟到一定阶段时,有必要打破现行著作权法的规定给予第二类生成物著作权法上的保护。