Design and Research of an Intelligent Learning System for University Physics

In order to break through the limitations of traditional teaching,realize the integration of online and offline teaching,and optimize the intelligent learning experience of university physics,this paper proposes the design of an intelligent learning system for university physics based on cloud computing platforms,and applies this system to teaching environment of university physics.It successfully integrates emerging technologies such as cloud computing,machine learning,and situational awareness,integrates learning context awareness,intelligent recording and broadcasting,resource sharing,learning performance prediction,and content planning and recommendation,and comprehensively improves the quality of university physics teaching.It can optimize the teaching process and deepen intelligent teaching reform,aiming at providing references for the teaching practice of university physics.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’Online Learning Engagement

As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.

Transfer learning from T1-weighted to T2-weighted Magnetic resonance sequences for brain image segmentation

Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and conditions.Due to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning networks.The aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation resources.With the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer learning.Moreover,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique.The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources.The segmentation results are evaluated using 14 clinical 3D brain MR and CT images.The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±0.0007.Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.

Transfer force perception skills to robot‐assisted laminectomy via imitation learning from human demonstrations

A comparative study of two force perception skill learning approaches for robot‐assisted spinal surgery,the impedance model method and the imitation learning(IL)method,is presented.The impedance model method develops separate models for the surgeon and patient,incorporating spring‐damper and bone‐grinding models.Expert surgeons'feature parameters are collected and mapped using support vector regression and image navi-gation techniques.The imitation learning approach utilises long short‐term memory networks(LSTM)and addresses accurate data labelling challenges with custom models.Experimental results demonstrate skill recognition rates of 63.61%-74.62%for the impedance model approach,relying on manual feature extraction.Conversely,the imitation learning approach achieves a force perception recognition rate of 91.06%,outperforming the impedance model on curved bone surfaces.The findings demonstrate the potential of imitation learning to enhance skill acquisition in robot‐assisted spinal surgery by eliminating the laborious process of manual feature extraction.

Implicit policy constraint for offline reinforcement learning

Offline reinforcement learning(RL)aims to learn policies entirely from passively collected datasets,making it a data‐driven decision method.One of the main challenges in offline RL is the distribution shift problem,which causes the algorithm to visit out‐of‐distribution(OOD)samples.The distribution shift can be mitigated by constraining the divergence between the target policy and the behaviour policy.However,this method can overly constrain the target policy and impair the algorithm's performance,as it does not directly distinguish between in‐distribution and OOD samples.In addition,it is difficult to learn and represent multi‐modal behaviour policy when the datasets are collected by several different behaviour policies.To overcome these drawbacks,the au-thors address the distribution shift problem by implicit policy constraints with energy‐based models(EBMs)rather than explicitly modelling the behaviour policy.The EBM is powerful for representing complex multi‐modal distributions as well as the ability to distinguish in‐distribution samples and OODs.Experimental results show that their method significantly outperforms the explicit policy constraint method and other base-lines.In addition,the learnt energy model can be used to indicate OOD visits and alert the possible failure.

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense effectiveness.Therefore,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense strategies.the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.

Evaluating Pharmacological and Rehabilitation Strategies for Effective Management of Bipolar Disorder: A Comprehensive Clinical Study

Bipolar disorder presents significant challenges in clinical management, characterized by recurrent episodes of depression and mania often accompanied by impairment in functioning. This study investigates the efficacy of pharmacological interventions and rehabilitation strategies to improve patient outcomes and quality of life. Utilizing a randomized controlled trial with multiple treatment arms, participants will receive pharmacotherapy, polypharmacotherapy, rehabilitation interventions, or combination treatments. Outcome measures will be assessed using standardized scales, including the Hamilton Depression Scale, Yale-Brown Obsessive Compulsive Scale (Y-BOCS), and Mania Scale. Preliminary data suggest improvements in symptom severity and functional outcomes with combination treatments. This research aims to inform clinical practice, guide treatment decisions, and ultimately enhance the quality of care for individuals living with bipolar disorder. Findings will be disseminated through peer-reviewed journals and scientific conferences to advance knowledge in this field.

A comprehensive review on deep learning approaches in wind forecasting applications

The effective use of wind energy is an essential part of the sustainable development of human society,in particular,at the recent unprecedented pressure in shaping a low carbon energy environment.Accurate wind resource and power forecasting play a key role in improving the wind penetration.However,it has not been well adopted in the real-world applications due to the strong stochastic characteristics of wind energy.In recent years,the application boost of deep learning methods provides new effective tools in wind forecasting.This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy.Featured approaches include timeseries-based recurrent neural networks,restricted Boltzmann machines,convolutional neural networks as well as auto-encoder-based approaches.In addition,future development directions of deep-learning-based wind energy forecasting have also been discussed.

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.

Teaching Design on Digital Environment of Intelligence Learning

The practice and research on the establishment of digital course environment and the reform of learning way breaks through the integration of information technology and teaching courses. Digital course environment promotes the teaching interest and becomes the learning under the support of learning terminal, which must make students happy. The digitalization of teaching materials, platform of learning and learning according to requirements promotes the establishment of happy class. Establishment of learning environment is the basis of realizing the reform of learning and teaching ways. With the rapid development of information technology, intelligence learning is the necessary outcome of deep integration with education learning.

D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

Epilepsy is considered as a serious brain disorder in which patients frequently experience seizures.The seizures are defined as the unexpected electrical changes in brain neural activity,which leads to unconsciousness.Existing researches made an intense effort for predicting the epileptic seizures using brain signal data.However,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's reliability.In addition,the existing prediction models have severe issues,such as overfitting and false positive rates.To overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain signals.Deep neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict ES.The multi‐network design enhances the trained model's generalisability and stability efficiently.Also,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal classification.The earlier warning of epilepsy with the proposed model obtains the auxiliary diagnosis.The data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing techniques.To be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%respectively.Overall,the numerical results unveil that the proposed work outperforms the existing models.

Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features

Recurrence is the key factor affecting the prognosis of osteosarcoma.Currently,there is a lack of clinically useful tools to predict osteosarcoma recurrence.The application of pathological images for artificial intelligence‐assisted accurate prediction of tumour out-comes is increasing.Thus,the present study constructed a quantitative histological image classifier with tumour nuclear features to predict osteosarcoma outcomes using haema-toxylin and eosin(H&E)‐stained whole‐slide images(WSIs)from 150 osteosarcoma patients.We first segmented eight distinct tissues in osteosarcoma H&E‐stained WSIs,with an average accuracy of 90.63%on the testing set.The tumour areas were auto-matically and accurately acquired,facilitating the tumour cell nuclear feature extraction process.Based on six selected tumour nuclear features,we developed an osteosarcoma histological image classifier(OSHIC)to predict the recurrence and survival of osteo-sarcoma following standard treatment.The quantitative OSHIC derived from tumour nuclear features independently predicted the recurrence and survival of osteosarcoma patients,thereby contributing to precision oncology.Moreover,we developed a fully automated workflow to extract quantitative image features,evaluate the diagnostic values of feature sets and build classifiers to predict osteosarcoma outcomes.Thus,the present study provides a novel tool for predicting osteosarcoma outcomes,which has a broad application prospect in clinical practice.

High resolution 3D nonlinear integrated inversion

The high resolution 3D nonlinear integrated inversion method is based on nonlinear theory. Under layer control, the log data from several wells （or all wells） in the study area and seismic trace data adjacent to the wells are input to a network with multiple inputs and outputs and are integratedly trained to obtain an adaptive weight function of the entire study area. Integrated nonlinear mapping relationships are built and updated by the lateral and vertical geologic variations of the reservoirs. Therefore, the inversion process and its inversion results can be constrained and controlled and a stable seismic inversion section with high resolution with velocity inversion, impedance inversion, and density inversion sections, can be gained. Good geologic effects have been obtained in model computation tests and real data processing, which verified that this method has high precision, good practicality, and can be used for quantitative reservoir analysis.

Artificial Intelligence Trends and Ethics: Issues and Alternatives for Investors

Artificial intelligence (AI) based technology, machine learning, and cognitive systems have played a very active role in society’s economic and technological transformation. For industrial value chains and international businesses, it means that a structural change is necessary since these machines can learn and apply new information in making forecasts, processing, and interacting with people. Artificial intelligence (AI) is a science that uses powerful enough techniques, strategies, and mathematical modelling to tackle complex actual problems. Because of its inevitable progress further into the future, there have been considerable safety and ethical concerns. Creating an environment that is AI friendly for the people and vice versa might be a solution for humans and machines to discover a common set of values. In this context, the goal of this study is to investigate the emerging trends of AI (the benefits that it brings to the society), the moral challenges that come from ethical algorithms, learned or pre-set ideals, as well as address the ethical issues and malpractices of AI and AI security. This paper will address the consequences of AI in relation to investors and financial services. The article will examine the challenges and possible alternatives for resolving the potential unethical issues in finance and will propose the necessity of new AI governance mechanisms to protect the efficiency of the capital markets as well as the role of financial authority in the regulation and monitoring of the huge expansion of AI in finance.

Visual interpretability for deep learning：a survey

This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations.Although deep neural networks have exhibited superior performance in various tasks,interpretability is always Achilles' heel of deep neural networks.At present,deep neural networks obtain high discrimination power at the cost of a low interpretability of their black-box representations.We believe that high model interpretability may help people break several bottlenecks of deep learning,e.g.,learning from a few annotations,learning via human–computer communications at the semantic level,and semantically debugging network representations.We focus on convolutional neural networks(CNNs),and revisit the visualization of CNN representations,methods of diagnosing representations of pre-trained CNNs,approaches for disentangling pre-trained CNN representations,learning of CNNs with disentangled representations,and middle-to-end learning based on model interpretability.Finally,we discuss prospective trends in explainable artificial intelligence.

Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.

Revisiting the ODE Method for Recursive Algorithms:Fast Convergence Using Quasi Stochastic Approximation

Several decades ago,Profs.Sean Meyn and Lei Guo were postdoctoral fellows at ANU,where they shared interest in recursive algorithms.It seems fitting to celebrate Lei Guo’s 60 th birthday with a review of the ODE Method and its recent evolution,with focus on the following themes:The method has been regarded as a technique for algorithm analysis.It is argued that this viewpoint is backwards:The original stochastic approximation method was surely motivated by an ODE,and tools for analysis came much later(based on establishing robustness of Euler approximations).The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time,following by careful approximation to obtain a practical recursive algorithm.While these methods are usually presented in a stochastic setting,this is not a prerequisite.In fact,recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo.Subject to conditions,the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert.The conditions are not universal,but theory suggests alternatives to achieve acceleration.The theory is illustrated with applications to gradient-free optimization,and policy gradient algorithms for reinforcement learning.

Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.