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.

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.

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.

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%.

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%.

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.

COVID-19 Pandemic Data Predict the Stock Market

Unlike the 2007–2008 market crash,which was caused by a banking failure and led to an economic recession,the 1918 influenza pandemic triggered a worldwidefinancial depression.Pandemics usually affect the global economy,and the COVID-19 pandemic is no exception.Many stock markets have fallen over 40%,and companies are shutting down,ending contracts,and issuing volun-tary and involuntary leaves for thousands of employees.These economic effects have led to an increase in unemployment rates,crime,and instability.Studying pandemics’economic effects,especially on the stock market,has not been urgent or feasible until recently.However,with advances in artificial intelligence(AI)and the inter-connectivity that social media provides,such research has become possible.In this paper,we propose a COVID-19-based stock market prediction system(C19-SM2)that utilizes social media.Our AI system enables economists to study how COVID-19 pandemic data influence social media and,hence,the stock market.C19-SM2 gathers COVID-19 infection and death cases reported by the authorities and social media data from a geographic area and extracts the sentiments and events that occur in that area.The information is then fed to the support vector machine(SVM)and random forest and random tree classifiers along with current stock market values.Then,the system produces a projection of the stock market’s movement during the next day.We tested the system with the Dow Jones Industrial Average(DJI)and the Tadawul All Share Index(TASI).Our system achieved a stock market prediction accuracy of 99.71%,substantially higher than the 89.93%accuracy reported in the related literature;the inclusion of COVID-19 data improved accuracy by 9.78%.

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states.At the same time,latest develop-ments of artificial intelligence(AI)techniques have the ability to manage and ana-lyzing massive amounts of biomedical datasets results in clinical decisions and real time applications.They can be employed for medical imaging;however,the 1D biomedical signal recognition process is still needing to be improved.Electrocardiogram(ECG)is one of the widely used 1-dimensional biomedical sig-nals,which is used to diagnose cardiovascular diseases.Computer assisted diag-nostic modelsfind it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals.To resolve these issues,this study designs automated deep learning based 1D biomedical ECG sig-nal recognition for cardiovascular disease diagnosis(DLECG-CVD)model.The DLECG-CVD model involves different stages of operations such as pre-proces-sing,feature extraction,hyperparameter tuning,and classification.At the initial stage,data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing.In addition,deep belief network(DBN)model is applied to derive a set of feature vectors.Besides,improved swallow swarm optimization(ISSO)algorithm is used for the hyper-parameter tuning of the DBN model.Lastly,extreme gradient boosting(XGBoost)classifier is employed to allocate proper class labels to the test ECG signals.In order to verify the improved diagnostic performance of the DLECG-CVD model,a set of simulations is carried out on the benchmark PTB-XL dataset.A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy,sensitivity,specificity,kappa,Mathew correlation coefficient,and Hamming loss.

Research on Driver Monitoring Systems Based on Vital Signs and Behavior Detection

Aiming at drivers’dangerous driving behavior monitoring and health monitoring,this paper designs an intelligent steering wheel that can monitor dan-gerous driving behavior and a steering wheel sleeve that can monitor physical health.The MTCNN model is primarily used to obtain a driver’s face image in real time.The PFLD algorithm was used to obtain the facial model positioning feature points,and the degree of driver fatigue was determined by combining the relevant parameters.The fatigue algorithm proposed in this paper can improve the effectiveness and accuracy of monitoring.Then,according to the LSTM network model,11 groups of key point information of the human body are obtained,and the human motion track is identified and then combined with the facial information to complete the judgment of driving behavior such as drinking water,smoking and walking.Through the PPG and ECG fusion algorithm based on LSTM,the reliability of the system to collect vital signs such as body temperature,blood pressure,heart rate and blood oxygen of the driver is improved.It was determined that the system could monitor a driver’s driving behavior in real time and consider its health management.

Research and Application of AI-Enabled Education

Artificial intelligence technology has developed rapidly in variousfields and has been widely used.Education and teaching are also areas in which artificial intelligence is applied.Research on artificial intelligence-enabled(AI-enabled)education and teaching is emerging,such as educational data mining and intelligent assisted teaching systems.First,research on AI-enabled education is introduced,and then the differences between AI-enabled education and tradi-tional education and cases of educational data mining,learning prediction,learn-ing resource recommendation,and various intelligent-assisted teaching systems are analysed.Our existing research results and future development are proposed,such as research on online learning session dropout prediction and the design and implementation of the zhixin teaching assistance system.Finally,this paper concludes that artificial intelligence has been well integrated into education and teaching activities in various ways and has improved students’learning experience and teachers’teaching quality.AI-enabled education and teaching is efficient and will play an increasingly important role.

Revealing the real-time diversity and abundance of small mammals by using an Intelligent Animal Monitoring System(IAMS)

It is challenging to reveal the real-time spatio-temporal change of diversity and abundance of animals in natural systems by using traditional methods.The rapid advancement of new technologies such as the Internet of Things,artificial intelligence,and big-data processing,provide opportunities for developing novel technologies for moni-toring biodiversity and population abundance of animals with high efficacy and accuracy.In this study,by using a recently developed Intelligent Animal Monitoring System,named“Vector Intelligent Monitoring System(VIMS)”,we investigated the real-time diversity and abundance of small mammals in the Banruosi forest,Dujiangyan region,southwest China.To make a comparison of the VIMS with traditional methods,we also surveyed the diversity and abundance of small mammals using wired live traps.Compared to live traps,the VIMS has several advantages such as automatic data collection,intelligent identification of species,data visualization,whole-day and all-weather op-eration,little disturbance to animals,real-time monitoring,and is capable of revealing more small mammal species.However,the VIMS also has several disadvantages over live traps such as lower trapping efficiency and being more expensive than live traps.Our results suggest that the VIMS can be a complementary method to traditional ones in monitoring the real-time spatio-temporal change of diversity and abundance of small mammals(especially rare species).In addition,the VIMS is useful in monitoring other small animals like small carnivores,birds,amphibians,and reptiles.