When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ...When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.展开更多
The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizat...The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.展开更多
The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and ...The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.展开更多
Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes...Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.展开更多
The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several nations.Nearly every country around the globe is now very concerne...The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several nations.Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese residents.Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems.Every time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will stop.The prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a model.Extended short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term dependencies.As an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for COVID-19.We provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring framework.We developed a data analysis methodology,including data preparation and dataset splitting.We examine two popular algorithms,LSTM and Decision tree on the official datasets.Moreover,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the pandemic.Key issues and challenges are discussed for future improvement.It is expected that the results these methods provide for the Health Scenario would be reliable and credible.展开更多
Interdisciplinary applications between information technology and geriatrics have been accelerated in recent years by the advancement of artificial intelligence,cloud computing,and 5G technology,among others.Meanwhile...Interdisciplinary applications between information technology and geriatrics have been accelerated in recent years by the advancement of artificial intelligence,cloud computing,and 5G technology,among others.Meanwhile,applications developed by using the above technologies make it possible to predict the risk of age-related diseases early,which can give caregivers time to intervene and reduce the risk,potentially improving the health span of the elderly.However,the popularity of these applications is still limited for several reasons.For example,many older people are unable or unwilling to use mobile applications or devices(e.g.smartphones)because they are relatively complex operations or time-consuming for older people.In this work,we design and implement an end-to-end framework and integrate it with the WeChat platform to make it easily accessible to elders.In this work,multifactorial geriatric assessment data can be collected.Then,stacked machine learning models are trained to assess and predict the incidence of common diseases in the elderly.Experimental results show that our framework can not only provide more accurate prediction(precision:0.8713,recall:0.8212)for several common elderly diseases,but also very low timeconsuming(28.6 s)within a workflow compared to some existing similar applications.展开更多
With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering s...With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.展开更多
Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes dif...Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security.Data is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our reality.In the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied level.The paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.展开更多
Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a mo...Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%.展开更多
This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained mode...This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.展开更多
The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniqu...The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniques such as spectrum sensing,spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index,frequency bands,coding rate etc.,to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency,high power consumption and more chip area utilisation.To overcome the problems mentioned above,we present DEEP HOLE Radio sys-tems,the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency(RF)environment.DEEP HOLE Radio provides(i)an opti-mized deep learning framework with a good trade-off between latency,power and utilization.(ii)Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni direc-tional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models,which run parallel on Zynq-SoC 7000,consuming less area,power,latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn,Long Term Short Memory(LSTM),Convolutional Neural Networks(CNN)and Deep Neural Networks(DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area,power and time.More-over,the experimental results show that the proposed framework decreases the delay,power and area by 15%,20%25%concerning the existing RFlearn and other hardware constraint frameworks.展开更多
In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroi...In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.展开更多
This essay which is dedicated into doing action research explores the difficulties in conducting the collaborative learning in a student-centered classroom teaching,mostly based on teachers’reflection.This essay adap...This essay which is dedicated into doing action research explores the difficulties in conducting the collaborative learning in a student-centered classroom teaching,mostly based on teachers’reflection.This essay adapts a framework of conducting,monitoring and evaluating collaborative learning into specific teaching context and points out research areas in the next step of action research.展开更多
Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were ...Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus.Such enforced alienation affected both the mental and social condition of people significantly.Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets.The pandemic has shoved the entire planet into an unstable state.The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia.To achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the society.We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society.Findings of this research study indicate that financial resources were the worst affected.Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people.Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.展开更多
The recent emergence of adaptive language learning systems calls for conceptual work to guide the design of assessment and learning in an adaptive environment.Although adaptive learning might have been touted as a uni...The recent emergence of adaptive language learning systems calls for conceptual work to guide the design of assessment and learning in an adaptive environment.Although adaptive learning might have been touted as a universal cure for learning problems,many adaptive language learning systems fall short of educators’expectations,partly due to a lack of standards and best practices in this area.To fill this gap,this paper proposes some major considerations in designing a high-quality assessment and learning experience in adaptive learning and ways to evaluate an adaptive learning system.The architecture of adaptive learning is decomposed,with a chain of inferences supporting the overall efficacy of an adaptive learning system presented,including user property representation,user property estimation,content representation,user interaction representation,and user interaction impact.A detailed analysis of key validity issues is provided for each inference,which motivates the major considerations in designing and evaluating assessment and learning.The paper first provides an overview of different types of assessment used in adaptive learning and an analysis of the assessment approach,priorities,and design considerations of each to optimize its use in adaptive learning.Then it proposes a framework for evaluating different aspects of an adaptive learning system.Some special connections are made to models,techniques,designs,and technologies specific to language learning and assessment,bringing more relevance to adaptive language learning solutions.Through establishing some guidelines on key aspects to evaluate and how to evaluate them,the work intends to bring more rigor to the field of adaptive language learning systems.展开更多
基金the R&D&I,Spain grants PID2020-119478GB-I00 and,PID2020-115832GB-I00 funded by MCIN/AEI/10.13039/501100011033.N.Rodríguez-Barroso was supported by the grant FPU18/04475 funded by MCIN/AEI/10.13039/501100011033 and by“ESF Investing in your future”Spain.J.Moyano was supported by a postdoctoral Juan de la Cierva Formación grant FJC2020-043823-I funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR.J.Del Ser acknowledges funding support from the Spanish Centro para el Desarrollo Tecnológico Industrial(CDTI)through the AI4ES projectthe Department of Education of the Basque Government(consolidated research group MATHMODE,IT1456-22)。
文摘When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.
文摘The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.
文摘The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.
文摘Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.
基金The authors are grateful to the Taif University Researchers Supporting Project Number(TURSP-2020/36),Taif University,Taif,Saudi Arabia.
文摘The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several nations.Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese residents.Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems.Every time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will stop.The prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a model.Extended short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term dependencies.As an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for COVID-19.We provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring framework.We developed a data analysis methodology,including data preparation and dataset splitting.We examine two popular algorithms,LSTM and Decision tree on the official datasets.Moreover,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the pandemic.Key issues and challenges are discussed for future improvement.It is expected that the results these methods provide for the Health Scenario would be reliable and credible.
基金supported by Xi’an University of Finance and Economics Scientific Research Support Program(No.21FCZD03)Shaanxi Education Department Research Program(No.22JK0077)National Statistical Science Research Project(Nos.2021LZ40,2022LZ38)。
文摘Interdisciplinary applications between information technology and geriatrics have been accelerated in recent years by the advancement of artificial intelligence,cloud computing,and 5G technology,among others.Meanwhile,applications developed by using the above technologies make it possible to predict the risk of age-related diseases early,which can give caregivers time to intervene and reduce the risk,potentially improving the health span of the elderly.However,the popularity of these applications is still limited for several reasons.For example,many older people are unable or unwilling to use mobile applications or devices(e.g.smartphones)because they are relatively complex operations or time-consuming for older people.In this work,we design and implement an end-to-end framework and integrate it with the WeChat platform to make it easily accessible to elders.In this work,multifactorial geriatric assessment data can be collected.Then,stacked machine learning models are trained to assess and predict the incidence of common diseases in the elderly.Experimental results show that our framework can not only provide more accurate prediction(precision:0.8713,recall:0.8212)for several common elderly diseases,but also very low timeconsuming(28.6 s)within a workflow compared to some existing similar applications.
基金partially funded by the 2023 Teaching Quality Engineering Construction Project of Sun Yat-sen University(No.76250-12230036)the 2023 Project of Computer Education Research Association of Chinese Universities(No.CERACU2023R02)。
文摘With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.
基金supported by National Natural Science Foundation of China (NO.51974131)Hebei Province Natural Science Fund for Distinguished Young Scholars (NO.E2020209082).
文摘Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security.Data is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our reality.In the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied level.The paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.
文摘Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%.
基金This work was supported by the Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”of China under Grant 2018AAA0102303the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20190030)the National Natural Science Foundation of China(No.61631020,No.61871398,No.61931011 and No.U20B2038).
文摘This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.
文摘The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniques such as spectrum sensing,spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index,frequency bands,coding rate etc.,to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency,high power consumption and more chip area utilisation.To overcome the problems mentioned above,we present DEEP HOLE Radio sys-tems,the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency(RF)environment.DEEP HOLE Radio provides(i)an opti-mized deep learning framework with a good trade-off between latency,power and utilization.(ii)Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni direc-tional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models,which run parallel on Zynq-SoC 7000,consuming less area,power,latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn,Long Term Short Memory(LSTM),Convolutional Neural Networks(CNN)and Deep Neural Networks(DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area,power and time.More-over,the experimental results show that the proposed framework decreases the delay,power and area by 15%,20%25%concerning the existing RFlearn and other hardware constraint frameworks.
文摘In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.
文摘This essay which is dedicated into doing action research explores the difficulties in conducting the collaborative learning in a student-centered classroom teaching,mostly based on teachers’reflection.This essay adapts a framework of conducting,monitoring and evaluating collaborative learning into specific teaching context and points out research areas in the next step of action research.
基金Funding for this study was received from the Ministry of Education andDeanship of Scientific Research at King Abdulaziz University, Kingdom of Saudi Arabia underthe Grant No. IFPHI-267-611-2020.
文摘Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus.Such enforced alienation affected both the mental and social condition of people significantly.Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets.The pandemic has shoved the entire planet into an unstable state.The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia.To achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the society.We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society.Findings of this research study indicate that financial resources were the worst affected.Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people.Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.
文摘The recent emergence of adaptive language learning systems calls for conceptual work to guide the design of assessment and learning in an adaptive environment.Although adaptive learning might have been touted as a universal cure for learning problems,many adaptive language learning systems fall short of educators’expectations,partly due to a lack of standards and best practices in this area.To fill this gap,this paper proposes some major considerations in designing a high-quality assessment and learning experience in adaptive learning and ways to evaluate an adaptive learning system.The architecture of adaptive learning is decomposed,with a chain of inferences supporting the overall efficacy of an adaptive learning system presented,including user property representation,user property estimation,content representation,user interaction representation,and user interaction impact.A detailed analysis of key validity issues is provided for each inference,which motivates the major considerations in designing and evaluating assessment and learning.The paper first provides an overview of different types of assessment used in adaptive learning and an analysis of the assessment approach,priorities,and design considerations of each to optimize its use in adaptive learning.Then it proposes a framework for evaluating different aspects of an adaptive learning system.Some special connections are made to models,techniques,designs,and technologies specific to language learning and assessment,bringing more relevance to adaptive language learning solutions.Through establishing some guidelines on key aspects to evaluate and how to evaluate them,the work intends to bring more rigor to the field of adaptive language learning systems.