Algorithms are the primary component of Artificial Intelligence(AI).The algorithm is the process in AI that imitates the human mind to solve problems.Currently evaluating the performance of AI is achieved by evaluatin...Algorithms are the primary component of Artificial Intelligence(AI).The algorithm is the process in AI that imitates the human mind to solve problems.Currently evaluating the performance of AI is achieved by evaluating AI algorithms by metric scores on data sets.However the evaluation of algorithms in AI is challenging because the evaluation of the same type of algorithm has many data sets and evaluation metrics.Different algorithms may have individual strengths and weaknesses in evaluation metric scores on separate data sets,lacking the credibility and validity of the evaluation.Moreover,evaluation of algorithms requires repeated experiments on different data sets,reducing the attention of researchers to the research of the algorithms itself.Crucially,this approach to evaluating comparative metric scores does not take into account the algorithm’s ability to solve problems.And the classical algorithm evaluation of time and space complexity is not suitable for evaluating AI algorithms.Because classical algorithms input is infinite numbers,whereas AI algorithms input is a data set,which is limited and multifarious.According to the AI algorithm evaluation without response to the problem solving capability,this paper summarizes the features of AI algorithm evaluation and proposes an AI evaluation method that incorporates the problem-solving capabilities of algorithms.展开更多
The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collec...The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University.The first step’s initial population placements were created using Particle Swarm Optimization(PSO).Then,using adaptive feature space search,Educational Grey Wolf Optimization(EGWO)was employed to choose the optimal attribute combination.The second stage uses the SVMclassifier to forecast classification accuracy.Different classifiers were utilized to evaluate the performance of students.According to the results,it was revealed that AI could forecast the final grades of students with an accuracy rate of 97%on the test dataset.Furthermore,the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50%and could be categorized as having equal information ratio gain after the semester.While the random forest provided an accuracy of 28%.These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy(12%).The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance,reducing chances of failure,and taking appropriate steps at the right time to raise the standards of education.The study also motivates academics to assess and discover EDM at several other universities.展开更多
Full-body avatar reconstruction offers users immersive and interactive experiences in virtual space,which are crucial for the advancement of metaverse applications.However,traditional hardware solutions,reliant on opt...Full-body avatar reconstruction offers users immersive and interactive experiences in virtual space,which are crucial for the advancement of metaverse applications.However,traditional hardware solutions,reliant on optical cameras or inertial sensors,are hampered by privacy concerns,spatial limitations,high costs,and calibration challenges.Here,we propose AI-enabled smart clothing that seamlessly integrates triboelectric strain-sensing fibers(TSSFs)and AI algorithms with commercial fitness suits to achieve precise dynamic 3D reconstruction of body movement.TSSFs enable the dynamic capture of body postures and excel in sensitivity,linearity,and strain range,while maintaining mechanical stability,temperature resilience,and washability.The integrated algorithms accurately decouple posture signals—distinguishing between similar postures with the 1D-CNN algorithm,compensating for body-shape differences via a calibration algorithm,and determining spatial elements for avatar reconstruction using a decision-tree algorithm.Finally,leveraging Unity-3D,we achieve ultra-accurate dynamic 3D avatars with a joint angle error of<3.63°and demonstrate their effectiveness using VR fitness and enter-tainment applications,showing how they can offer users standardized yet engaging experiences.展开更多
The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN).The potential application of artificial intelligence(AI)alg...The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN).The potential application of artificial intelligence(AI)algorithms in these areas of clinical research has not been reported previously.The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs.A total of 149 wide-angle fundus photographs from40 eyes of 32 ARN patients were collected,and the U-Net method was used to construct the AI algorithm.Thereby,a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time.This algorithm had an area under the receiver operating curve of 0.92,with 86%sensitivity and 88%specificity in the detection of retinal necrosis.For the purpose of retinal necrosis evaluation,necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples(R2=0.7444,P<0.0001)and therapeutic response of ARN(R2=0.999,P<0.0001).Therefore,our AI algorithm has a potential application in the clinical aided diagnosis of ARN,evaluation of ARN severity,and treatment response monitoring.展开更多
Cell identification and sorting have been hot topics recently.However,most conventional approaches can only predict the category of a single target,and lack the ability to perform multitarget tasks to provide coordina...Cell identification and sorting have been hot topics recently.However,most conventional approaches can only predict the category of a single target,and lack the ability to perform multitarget tasks to provide coordinate information of the targets.This limits the development of high-throughput cell screening technologies.Fortunately,artificial intelligence(AI)systems based on deep-learning algorithms provide the possibility to extract hidden features of cells from original image information.Here,we demonstrate an AI-assisted multitarget processing system for cell identification and sorting.With this system,each target cell can be swiftly and accurately identified in a mixture by extracting cell morphological features,whereafter accurate cell sorting is achieved through noninvasive manipulation by optical tweezers.The AI-assisted model shows promise in guiding the precise manipulation and intelligent detection of high-flux cells,thereby realizing semiautomatic cell research.展开更多
基金funded by the General Program of the National Natural Science Foundation of China grant number[62277022].
文摘Algorithms are the primary component of Artificial Intelligence(AI).The algorithm is the process in AI that imitates the human mind to solve problems.Currently evaluating the performance of AI is achieved by evaluating AI algorithms by metric scores on data sets.However the evaluation of algorithms in AI is challenging because the evaluation of the same type of algorithm has many data sets and evaluation metrics.Different algorithms may have individual strengths and weaknesses in evaluation metric scores on separate data sets,lacking the credibility and validity of the evaluation.Moreover,evaluation of algorithms requires repeated experiments on different data sets,reducing the attention of researchers to the research of the algorithms itself.Crucially,this approach to evaluating comparative metric scores does not take into account the algorithm’s ability to solve problems.And the classical algorithm evaluation of time and space complexity is not suitable for evaluating AI algorithms.Because classical algorithms input is infinite numbers,whereas AI algorithms input is a data set,which is limited and multifarious.According to the AI algorithm evaluation without response to the problem solving capability,this paper summarizes the features of AI algorithm evaluation and proposes an AI evaluation method that incorporates the problem-solving capabilities of algorithms.
基金supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University.The first step’s initial population placements were created using Particle Swarm Optimization(PSO).Then,using adaptive feature space search,Educational Grey Wolf Optimization(EGWO)was employed to choose the optimal attribute combination.The second stage uses the SVMclassifier to forecast classification accuracy.Different classifiers were utilized to evaluate the performance of students.According to the results,it was revealed that AI could forecast the final grades of students with an accuracy rate of 97%on the test dataset.Furthermore,the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50%and could be categorized as having equal information ratio gain after the semester.While the random forest provided an accuracy of 28%.These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy(12%).The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance,reducing chances of failure,and taking appropriate steps at the right time to raise the standards of education.The study also motivates academics to assess and discover EDM at several other universities.
基金supported by the National Natural Science Foundation of China(Grant No.62105238)。
文摘Full-body avatar reconstruction offers users immersive and interactive experiences in virtual space,which are crucial for the advancement of metaverse applications.However,traditional hardware solutions,reliant on optical cameras or inertial sensors,are hampered by privacy concerns,spatial limitations,high costs,and calibration challenges.Here,we propose AI-enabled smart clothing that seamlessly integrates triboelectric strain-sensing fibers(TSSFs)and AI algorithms with commercial fitness suits to achieve precise dynamic 3D reconstruction of body movement.TSSFs enable the dynamic capture of body postures and excel in sensitivity,linearity,and strain range,while maintaining mechanical stability,temperature resilience,and washability.The integrated algorithms accurately decouple posture signals—distinguishing between similar postures with the 1D-CNN algorithm,compensating for body-shape differences via a calibration algorithm,and determining spatial elements for avatar reconstruction using a decision-tree algorithm.Finally,leveraging Unity-3D,we achieve ultra-accurate dynamic 3D avatars with a joint angle error of<3.63°and demonstrate their effectiveness using VR fitness and enter-tainment applications,showing how they can offer users standardized yet engaging experiences.
基金the National Natural Science Foundation of China(Nos.81870648 and 82070949)。
文摘The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN).The potential application of artificial intelligence(AI)algorithms in these areas of clinical research has not been reported previously.The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs.A total of 149 wide-angle fundus photographs from40 eyes of 32 ARN patients were collected,and the U-Net method was used to construct the AI algorithm.Thereby,a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time.This algorithm had an area under the receiver operating curve of 0.92,with 86%sensitivity and 88%specificity in the detection of retinal necrosis.For the purpose of retinal necrosis evaluation,necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples(R2=0.7444,P<0.0001)and therapeutic response of ARN(R2=0.999,P<0.0001).Therefore,our AI algorithm has a potential application in the clinical aided diagnosis of ARN,evaluation of ARN severity,and treatment response monitoring.
基金supported by the National Natural Science Foundation of China(Nos.61975128,62175157,92150301,and 62375177)the Shenzhen Science and Technology Program(Nos.JCYJ20210324120403011 and RCJC20210609103232046)the Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030009)。
文摘Cell identification and sorting have been hot topics recently.However,most conventional approaches can only predict the category of a single target,and lack the ability to perform multitarget tasks to provide coordinate information of the targets.This limits the development of high-throughput cell screening technologies.Fortunately,artificial intelligence(AI)systems based on deep-learning algorithms provide the possibility to extract hidden features of cells from original image information.Here,we demonstrate an AI-assisted multitarget processing system for cell identification and sorting.With this system,each target cell can be swiftly and accurately identified in a mixture by extracting cell morphological features,whereafter accurate cell sorting is achieved through noninvasive manipulation by optical tweezers.The AI-assisted model shows promise in guiding the precise manipulation and intelligent detection of high-flux cells,thereby realizing semiautomatic cell research.
