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Implementation of Precision Education System Based on machine learning Model 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Shanmugasundar, G. Chohan, Jasgurpreet Singh Kalita, Kanak Department of Mechanical Engineering Sri Sai Ram Institute of Technology Chennai600044 India Department of Mechanical Engineering and University Center for Research & Development Chandigarh University Mohali140413 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Jadara University Research Center Jadara University Irbid21110 Jordan

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Precision Education (PE) is an approach that emphasizes the use of various forms of technology to personalize students’ educational opportunities according to their unique requirements. In order to facilitate effective learning that is suited to the needs of each individual, it is necessary to have complete information about the learning process. PE describes a personalized approach to classroom instruction. We propose Dignified Reinforcement learning (DRL) which includes all the essential elements required for PE, in order to determine the impact of developing multimodal innovations on individualized learning. the system aims to address issues with the standard model of education analysis, such as insufficient internal validity, predictive validity, timeliness, comparability, and understandability, in order to provide more precise instruction. Networking sites, the digital classroom, the intelligent agent, and the panel are the four fundamental aspects of an RL-based technology. through model-based RL, the head attribute of the continuous educational environment gathers and processes a multimodal flow of student records from the various modules to create laws of appropriate behaviors that optimize the long-term benefits for both the individual and the computer. To demonstrate the usefulness of the proposed model, experimental evidence was presented using a subset of multimodal data. Practical and theoretical implications for future digital learning approaches and investigations may be drawn from RL systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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machine learning Model Development Using Computational Neurology 1

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Kanrar, Soumen Department of Computer Science and Engineering Amity University Jharkhand Ranchi801001 India Department of Computer Science Vidyasagar University West Bengal Midnapore India

ISBN: (数字)9789811986697

ISBN: (纸本)9789811986680

the ever-enhancement of techniques and technology helps computer scientists to collect voluminous data with specified resolution. the bottleneck in understanding how computational neurology works is consequently shifting away from the amount and type of voluminous data. the general query raised by everyone mined is, what do we do with that voluminous data? there has been a growing interest in leveraging the vast volume of data. Computational neurology gains more insight into the voluminous data with the help of various levels of analysis, measurement techniques, and experimental paradigms. this paper presents the algorithmic development, implementation, and validation of a machine learning model based on the spike response model (SRM-0) of neuronal dynamics. We have considered 'spike, time-dependent plasticity’ (STDP) as our strategy for reinforcement learning in this work. the performance of the model is exhibited through the pattern recognition task of recognizing handwritten digits from the MNIST dataset. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Few-Shot learning for Medical Numerical Understanding Based on machine Reading Comprehension 7th

Few-Shot Learning for Medical Numerical Understanding Based ...

引用

7th international conference on Smart computing and Communication, SmartCom 2022

作者： Zeng, Xiaodong Hu, Wenhui Liu, Xueyang Chen, Yuhang Shao, Wenyu Sun, Lizhuang Peking University No. 5 YiHeYuan Road Haidian District Beijing China

ISBN: (纸本)9783031281235

Numerical understanding relies on some content understanding techniques, which can be based on rules, entity extraction, and machine reading comprehension. Traditional methods often require a large number of regular expressions or a large number of data annotations, and often do not have a deep understanding of numerical values, lacking the ability to distinguish similar numerical values. In this paper, we propose a few-shot learning framework for numerical understanding tasks in Chinese medical texts, and through dynamic negative sampling of the training data, the model’s ability to discriminate similar numerical values is enhanced. We use patient text data provided by 13 hospitals in Beijing to conduct experiments. the results show that our newly proposed method is superior to training the baseline pretrained language model directly, the EM increases by 38% and the F1 increases by 27.59%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Numerical methods

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Clustering With Adaptive Graph learning and Spectral Rotation 7

Clustering With Adaptive Graph Learning and Spectral Rotatio...

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7th international conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. this enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. the proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © the Institution of Engineering & Technology 2023.

关键词： Fuzzy clustering

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A Study on machine learning and Deep learning Techniques Applied in Predicting Chronic Kidney Diseases 1

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6th international conference on soft computing and Signal Processing, ICSCSP 2023

作者： Chapa, Kalyani Ravi, Bhramaramba Andhra Pradesh Visakhapatnam India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Chronic kidney disease (CKD) is one of the heterogeneous disorders in which the kidneys’ functionality degenerates over time. Although there is a range of abnormalities in kidney function, the malfunction going beyond a threshold leads to untreated kidney failure, also narrated as end-stage renal disorder. However, at times, high-end complex treatments such as kidney transplantation or dialysis may also be life-threatening in CKD patients. the situation often leads to irreversible kidney structure and function, which may also implicate cardio, endocrine, and xenobiotic toxic complications. CKD is identified as a decrease in GFR and/or a rise in albuminuria. As this health disorder becomes more prevalent, the quality of life index becomes detrimental. Moreover, the consequences impact the nation’s economy direct or indirectly. At this juncture, suitable preventive measures and strategic planning are imperative. On the other hand, the world is advancing with modern innovations. Artificial Intelligence, machine learning, and Deep learning are unique technologies exhaustively employed in every sector. these disruptive technologies did not exempt the health segment and even proved their supremacy in several contexts. Accurate disease prediction and early detection are among the outcomes that could be expected from these technologies, so preventive measures could be suggested beforehand. In this article, a comprehensive investigation done by distinguished researchers is explored and presented. Around 100 articles published during the past decade are part of our study, which are deep-dived, and the respective contributions are cited. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Computational learning Model for Prediction of Parkinson’s Disease Using machine learning 5th

Computational Learning Model for Prediction of Parkinson’s ...

引用

5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Swathi, Ch. Cheripelli, Ramesh Department of CSE G. Narayanamma Institute of Technology and Science Telangana Hyderabad India Department of IT G. Narayanamma Institute of Technology and Science Telangana Hyderabad India

ISBN: (纸本)9789811986680

Parkinson’s disease (PD) is a chronic and progressive condition;a wide variety of motor and cognitive symptoms may present. Parkinson’s disease symptoms may coexist with other illnesses such as age-related decline and essential tremor. In order to aid in the identification of this ailment, many researches have been conducted in the last few years. Deep learning, recursive neural networks, and convolutional neural networks will be employed in this study to draw a distinction between healthy individuals and those with PD. A secondary objective of this research is to see whether the PD test can better distinguish between individuals by analysing a variety of imaging and movement data sets (notably cube and spiral pentagon data sets). We’ll see time series or imaging data sets are better at detecting Parkinson’s disease as a result of this study. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Transfer learning for Computational Chaos System Based on Pool 7th

Transfer Learning for Computational Chaos System Based on Po...

引用

7th international conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Lu, Ruiting Zhang, Dongji Cui, Yingying Zhang, Yongping Ningbo University of Technology Zhejiang Ningbo315000 China Shanghai Institute of Technology Shanghai201418 China Chang’an University Shaanxi Xi’an710054 China

ISBN: (纸本)9789819927296

Can a neural network trained by the time series of system A be used to predict the evolution of system B? this question, known as transfer learning, has great significance in machine learning and data mining, but has not been solved in chaotic systems. this paper studies the transfer learning of chaotic systems from the perspective of synchronization theory, that is, using the reservoir computer trained by the periodic system A to infer the unmeasured variables of the chaotic system B, and the reservoir computer trained by the chaotic system A to infer the chaotic system B. unmeasured variable. And system A is different from chaotic system B in parameters and dynamics. the study found that if the parameters of system A and system B are different, the pool calculation can be well synchronized with system B. In this paper, the transfer learning of logistic mapping, henon mapping and Lorentz equation is used to study the information transfer on the computational coupling chain of the storage pool. Finally, the four-dimensional power system experiment shows that the information learned from the modeling system can be used for prediction Evolution of experimental systems. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chaotic systems

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Conversion of Sign Language to Text Using machine learning 7th

Conversion of Sign Language to Text Using Machine Learning

引用

7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Bhagwat, Aishwarya Gupta, Poonam Kadam, Nivedita Computer Engineering G.H Raisoni College of Engineering and Management Pune India

ISBN: (纸本)9789811954818

Hand motion detection is a critical step in the automatic translation of sign language, which allows deaf people to communicate. In the related discipline of Sign Language, a considerable study was done, and the same could be said for Indian Sign Language (ISL). In this paper, we proposed ISL detection and recognition using a machine learning framework. the convolutional neural network (CNN) has been used for detecting the activity using the ResNet-100 machine learning framework. the Indian Sign Language database contains 26 alphabets and 0–9 digits. the feature extraction and selection have been done in each convolutional layer, and optimization has been done in the pooling layer. Multiple activation functions have been utilized in dense layers for classification on real-time hand gesture as well as MNIST datasets. An extensive experimental analysis of proposed system and results have been evaluated with various machine learning algorithms that produce almost 5–7% higher accuracy with state-of-the-art methods. Sign language recognition is used to recognize alphabets, letters or words. Communication is a best way to exchange feelings. the deaf community have to be dependent on an interpreter to interact with normal people;due to this, they suffer extremely to interact with normal people. the aim of our system is to design a vision-based computer application that can convert the Indian Sign Language to text. We have discussed the dataset, system workflow, testing and training phases of convolutional neural network. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A Variational Quantum soft Actor-Critic Algorithm for Continuous Control Tasks 4th

A Variational Quantum Soft Actor-Critic Algorithm for Conti...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Policicchio, Antonio Acuto, Alberto Barillà, Paola Bozzolo, Ludovico Conterno, Matteo NTT DATA Italia S.p.A. Via Ernesto Calindri 4 Milan20143 Italy

ISBN: (纸本)9783031812460

Quantum computing promises the availability of computational resources and generalization capabilities well beyond the possibilities of classical computers. An interesting approach for leveraging the near-term, Noisy Intermediate-Scale Quantum Computers, is the hybrid training of Parameterized Quantum Circuits (PQCs), i.e. the optimization of a parameterized quantum algorithms as a function approximation with classical optimization techniques. When PQCs are used in machine learning models, they may offer some advantages over classical models in terms of memory consumption and sample complexity for classical data analysis. In this work we explore and assess the advantages of the application of Parametric Quantum Circuits to one of the state-of-art Reinforcement learning algorithm for continuous control - namely soft Actor-Critic. We investigate its performance on the control of a virtual robotic arm by means of digital simulations of quantum circuits. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further developments and studies. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further developments and studies. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum computers

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Comparative Analysis of machine learning Algorithms on Mental Health Dataset 7th

Comparative Analysis of Machine Learning Algorithms on Menta...

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7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Lakshmi Prasanna Mehrotra, Shashi Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India Faculty of Engineering College of Computing Science and Technology Teerthanker Mahaver University Moradabad India

ISBN: (纸本)9789819937578

Maintaining good health for the well-being of a person is very important. Due to prolonged working hours and hectic schedules, IT and non-IT professionals are prone to stress. Companies provide health incentives to employees to address this issue, which is insufficient to alleviate stress. this paper compiles data from Open Source Mental Illness (OSMI) on the working conditions of both IT and non-IT employees. the data was further analyzed to find the essential attributes affecting a person’s health. the paper explored the Decision tree, Bayesian classification and Logistic Regression techniques for predicting employee stress. Experimental results show the Decision tree classification achieves a better result than the Bayesian classification and Logistic Regression. With these findings, industries can now focus on reducing stress and create a more pleasant work environment for their employees. the primary objective of this study is to help the medical community by providing a novel method for generating relevant stress regarding rules and predicting a patient's stress level using machine learning algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Decision trees

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