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Artificial Intelligence Assisted Student learning and Performance Analysis using Instructor Evaluation Model 3

Artificial Intelligence Assisted Student Learning and Perfor...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Nithiyanandam, N. Dhanasekaran, S. Kumar, A. Sivanesh Gobinath, D. Vijayakarthik, P. Rajkumar, G. Vinoth Muthuraman, U. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Kalasalingam University Department of Information Technology Tamilnadu Srivilliputtur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Sri Manakulavinayagar Engineering College Department of Information Technology Puducherry India Sir M Visvesvaraya Institute of Technology Department of Information Science and Engineering Bengaluru India J P College of Engineering Department of Electronics and Communication Engineering Tamilnadu Tenkasi627852 India Francis Xavier Engineering College Dept of Eee Tirunelveli India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Student's proficiency in a subject may be gauged through the use of learning Management Systems (LMS) (LMSs). Using a scoring algorithm to calculate the percentage of each student's attentiveness in the specific class, this system aims to increase the use of artificial intelligence approaches. The graph provided by this tool may be used to measure how well a student understands the material being taught. data mining tools are increasingly being used in higher education to help students and administrators better understand and resolve educational and administrative difficulties. The majority of educational mining research focuses on replicating students' performance rather than instructors' performance. The course assessment questionnaire is a standard instrument for evaluating teachers' performance based on students' perceptions. There are a number of different ways for creating classification techniques in this system, from decision trees to support vector machines to machine learning techniques, including differential evolution. Comparing their answers to an actual course assessment question based on its accuracy, retention and sensitivity. Students' online conduct has become more accessible because to the use of learning Management Systems (LMSs) in educational institutions. These statistics have been utilized by several studies to predict student outcomes. A wide range of subjects and predictive characteristics collected from the LMS make it difficult to draw generalized statements about the processes that control student performance. To get started let's take a look at some of the most recent research' theoretical foundations and common predictions in the field of learning analytics. A total of 4,989 students took 17 blended courses with Moodle LMS at the same school, and both multi-level and conventional regressions are utilized to examine the impact of LMS predictor factors and between-course evaluation grades on student performance. Despite the fact that the

关键词： Students

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Enhancing Nucleotide pattern recognition: A Hybrid Encoding Approach with Bi-LSTM and GRU

Enhancing Nucleotide Pattern Recognition: A Hybrid Encoding ...

引用

3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Fatimah, Syeda Fizza Masood, Sarfaraz Rizvi, Danish Raza Department of Computer Engineering Jamia Millia Islamia University New Delhi110025 India

Gene sequence classification is based on the order of nucleotides present inside a DNA or RNA molecule. The nucleotide pattern recognition is challenging due to the complex structure and repetitive nature of gene sequences. Recent research on virus gene classification lacks a robust gene sequence classifier with parameter efficiency that can be evaluated on multiple datasets to produce efficient results. This work aims to utilize the computation power of RNN architectures Bi-LSTM and GRU to capture the sequential dependencies among nucleotides in viruses having variable length and complex pre-determined structure using a hybrid encoding approach by merging k-mer and label encoding techniques for data pre-processing. The results obtained for the RNN architectures were observed on 3 datasets with variable class sizes of 4, 6, and 14. The Bi-LSTM achieved accuracies of 96.11%, 98.82%, and 97.55% for datasets 1, 2, and 3, respectively. Similarly, the GRU architecture showed impressive results, with accuracies of 97.55%, 98.72%, and 97.54% across the same three datasets. The research outcome strongly suggests that, even though both GRU and LSTM produce nearly identical performance metrics values, the fine-tuned GRU network emerges as a better choice for classifying viruses' gene sequences due to 1,685,774 trainable parameters, which are significantly less when compared to Bi-LSTM which had 2,012,046 trainable parameters. This work contributes to the nucleotide pattern recognition using RNN architectures, primarily GRUs, that offer improved computational efficiency without compromised accuracy across datasets, therefore achieving a balance between accuracy and computational efficiency across several datasets. © 2025 The Author(s).

关键词： Recurrent neural networks

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Sentiment Analysis in Indian Regional Language (Kannada) 3

Sentiment Analysis in Indian Regional Language (Kannada)

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3rd international conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Suprith Patil, K.P. Anu, D. Kishan, K. Bhavyashree, G. Venkatesh, Abhishek Nitte Meenakshi Institute of Technology Dept. of AI and Data Science India Nitte Mahalinga Adyanthaya Memorial Institute of Technology Dept.of CSE India

ISBN: (纸本)9798350391244

Sentiment analysis, which focuses on identifying and quantifying emotions in text to capture a writer's sentiments, whether they are positive or negative, plays a pivotal role in natural language processing (NLP) and information retrieval. Existing methods predominantly use English translations and are centered on binary sentiment classification, overlooking the rich literary tradition and cultural significance of Kannada. Using techniques like tokenization, data cleansing, stop word removal, stemming, and machine learning models specifically tailored for Kannada sentiment analysis, this paper proposes a new machine learning approach for the sentiment analysis of Kannada texts. We evaluated various classifiers, such as Linear Support Vector Classifier, Logistic Regression, SGD, K-Nearest Neighbors, Multinomial Naive Bayes, and Random Forest, by employing TF-IDF for data preprocessing and feature extraction. © 2024 IEEE.

关键词： Emotion recognition

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Utilizing machine learning and data mining to Identify Diarrheal Diseases with the Decision Tree Method

Utilizing Machine Learning and Data Mining to Identify Diarr...

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international conference on Informatics and Computing (ICIC)

作者： Fuzy Yustika Manik T.H. F Harumy Dewi Sartika Ginting Suci Ramadani Anandhini Medianty Nababan Nur Wulan Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Sumatera Utara Indonesia Departement computerized accounting STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Informatic Engineering Universitas Harapan Medan Medan Sumatera Utara Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In Indonesia, diarrheal disease is still a global health problem. The degree of morbidity and mortality is quite high, ranking 3rd after TB and pneumonia. In developed countries, even though there have been improvements in people’s health and economy, the incidence of infectious diarrhea remains high and is still a health problem. The development of increasingly complex science encourages humans to learn data that can be processed and produce new information and knowledge. In computer science, machine learning (ML) facilitates computers to read and interpret previously existing data automatically. machine learning can be used to classify diseases, determine the type of disease, and predict a disease. The machine learning method used is a decision tree. The step is to collect a data set consisting of input variables in the form of symptoms and output variables in the form of types of diarrheal diseases. Then change the form of the data into a table which becomes a model tree by looking for the entropy and gain of each symptom. After the model is formed, change the tree into a rule and simplify the rule (pruning). The results of identifying diarrheal diseases using the decision tree method were excellent, with an accuracy of 96.4%. Then a simplification of the rules is also obtained from 20 rules from 17 symptoms to 5 rules with a depth of 4 levels. The most influential symptoms in forming rules are G10, G14, and G16.

关键词： Accuracy Pneumonia Input variables Mortality machine learning Entropy data models Decision trees Informatics Diseases

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Application of machine learning Algorithm in Art Field – Taking Oil Painting as an Example 3rd

Application of Machine Learning Algorithm in Art Field – Ta...

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3rd international conference on Big data and Security, ICBDS 2021

作者： Huang, Kun Jiang, Jianlong Anhui Xinhua University Anhui230088 China

ISBN: (纸本)9789811908514

Oil painting production is a very time-consuming task. This article uses the current generation confrontation network popular in machine learning to transfer the style of images, and directly convert real-world images into high-quality oil paintings. In view of the current popular AnimeGAN and CartoonGAN generative confrontation networks, there are problems such as serious loss of details and color distortion in image migration. In this paper, by introducing SE-Residual Block (squeeze excitation residual block), comic face detection mechanism and optimizing the loss function, a new BicycleGAN is proposed to solve the problem of serious loss of details in the AnimeGAN migration image. By adding DSConv (distributed offset convolution), SceneryGAN is proposed to speed up the training speed and eliminate the ambiguous pixel blocks in the CartoonGAN migration image. The experimental results show that compared with AnimeGAN and CartoonGAN, the method in this paper has a significant improvement in training speed, comic image generation quality, and image local realism. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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AI Based Adaptive QoS Optimization for IoT Devices in Healthcare Networks

AI Based Adaptive QoS Optimization for IoT Devices in Health...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： S. Karthika K. Juliana Gnanaselvi K. Palanisamy S. Umamaheswari Department of Computer Science& Business Systems Dr. N. G. P Institute of Technology Coimbatore India Department of Computer Science Rathinam College of Arts and Science Coimbatore India Department of CSE Karpagam Academy of Higher Education Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper proposes an artificial intelligence-based adaptive Quality of Service (QoS) optimization framework for Internet of Things (IoT) devices in healthcare networks. IoT devices, such wearable monitoring and remote diagnostic tools, struggle to maintain optimal performance due to fluctuating network circumstances and hardware limitations. The suggested system uses machine learning and deep learning techniques to dynamically update QoS settings in order to achieve consistent, low-latency, and energy-efficient data transport. The AI model forecasts congestion, distributes resources optimally, and enhances network performance by continuously learning from real-time data and network conditions. This customisable solution improves healthcare delivery by providing timely, continuous services, resulting in better patient outcomes and ensuring the long-term viability of IoT healthcare networks. The framework's adaptive nature addresses scalability and flexibility challenges in healthcare IoT systems by constantly learning and optimising performance as network conditions change. The simulation results demonstrate how well the proposed solution works to improve QoS, ensure seamless healthcare delivery, and improve patient outcomes in dynamic network situations. This study paves the way for AI-powered healthcare IoT networks that are more reliable, responsive, and intelligent.

关键词： Performance evaluation Adaptive systems Medical services Quality of service Throughput Real-time systems pattern recognition Internet of Things Convolutional neural networks Optimization

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mining Recessive Teaching Resources of University Information Based on machine learning 1

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3rd EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2021

作者： Jingya, Zheng Yan, Jichao Zhengzhou Technical College Zhengzhou450121 China

ISBN: (数字)9783030825652

ISBN: (纸本)9783030825645

The accuracy of mining implicit teaching resources in traditional universities is low, so a method of mining implicit teaching resources in universities based on machine learning is designed. Firstly, it designs the process of data mining, define the problem, collect and preprocess the data, execute the mining algorithm and then explain and evaluate it. The classification method of data mining is optimized. In this paper, the classification technology is neural network, and the artificial neural network unit is built by biological neuron structure, and the classification is completed by biological transfer and activation function. Finally, the machine learning algorithm is improved, and the ight is updated by introducing momentum scalar factor. In the contrast experiment, it chooses the data set and train the parameters, design the process of data mining, and count the relevant parameters of the data set. The experiment results show that the accuracy of the designed method is 4.03% higher than that of the traditional method. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Informatization

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machine learning Based Fault Distance Estimation of Underground Transmission Using Impedance Relay

Machine Learning Based Fault Distance Estimation of Undergro...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sri Chaithanya Mathi Pooja Naidu Tammisettti Shaik Rohid Akthar Manitha P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Fast development of electric transmission infrastructure necessitates efficient methods of transmission and distribution for reliable cost effectiveness. Between the two main types of transmission, Overhead Transmission Lines involve extensive maintenance cost, use of extensive protection schemes, earthing, and lightning protection. In contrast, Underground Transmission Lines with their insulated and securely laid design are highly resistant to external factors such as lightning, wind, and open conductor faults, showing higher reliability and efficiency. However, faults such as symmetrical and unsymmetrical faults can occur due to conductors being bound together. The traditional fault distance estimation techniques are often inefficient for high-power transmission when using impedance-based fault distance relays. The constant variation in fault characteristics leads to impedance fluctuations, and therefore, fault distance calculations become inaccurate. The current research introduces a machine learning-based Fault Distance Estimation system for Underground Transmission Lines using Impedance Relays to address the above-mentioned challenges. Through data fed into the training machine learning model based on various fault scenarios, data mining is extracted for predicting precise impedance in faulty states and accurately determining fault locations by utilizing trained models with high-performance K-Nearest Neighbour (KNN), Decision Trees (DT), Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANNs). Through these algorithms and approaches, implementations that emphasize optimal preprocessing, effective feature selection and tuning of specific hyperparameters determine their respective modeling efficiencies. The results show that the models generate very accurate distance estimations, allowing for more accurate fault locations and quick rectification. Not only does it bypass the deficiency of the method

关键词： Power transmission lines Costs Estimation Predictive models Fault location Conductors data models Impedance data mining Long short term memory

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Application of Wave Cluster Algorithm Based on Kernel Optimization in pattern recognition of High-Frequency Financial data

Application of Wave Cluster Algorithm Based on Kernel Optimi...

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Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Dazhi Yue Business School Xiamen Institute of Technology Xiamen China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

High-frequency financial data has the characteristics of high nonlinearity, non-stationarity and noise, which brings great challenges to traditional data mining algorithms. In this study, we propose a Wave Cluster algorithm based on kernel function optimization, which aims to improve its performance in pattern recognition of high-frequency financial data. The Wave Cluster algorithm has attracted much attention for its robustness to noise and multiresolution analysis capability, but it still has limitations when dealing with complex nonlinear patterns. In this paper, kernel-based functions are introduced to map the original high-dimensional financial data to a higher-dimensional feature space. This nonlinear transformation can effectively capture the potentially complex relationships in the data. Subsequently, the modified Wave Cluster algorithm is applied in the transformed feature space, and high-frequency trading data from the China stock market, including price, trading volume and order book information, are used in the empirical study. Experimental results show that compared with traditional methods, the proposed algorithm achieves significant improvement in the tasks of identifying market microstructure, detecting abnormal trading patterns and predicting short-term price movements. Especially during periods of high volatility, the algorithm shows greater stability and accuracy.

关键词： Accuracy Software algorithms Noise Time series analysis Clustering algorithms Prediction algorithms Stability analysis Kernel Stock markets Optimization

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Feature-based Method for Nurse Care Complex Activity recognition from Accelerometer Sensor 21

Feature-based Method for Nurse Care Complex Activity Recogni...

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ACM international Joint conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM international Symposium on Wearable Computers (ISWC)

作者： Sayem, Faizul Rakib Sheikh, Md Mamun Ahad, Md Atiqur Rahman Univ Dhaka Dhaka Bangladesh Osaka Univ Suita Osaka Japan

ISBN: (纸本)9781450384612

As the number of patients in hospitals is increasing day by day, proper monitoring and hospital care towards patients must be ensured. In this regard, Nurse care activity recognition can play a significant role in improving the existing healthcare system. Research in this domain is very challenging because nursing activities are very complex and troublesome than other normal activities. Nursing activities are dependent not only on nurses but also on the patients' various states of illness. As a result, each activity has a high intra-class variation. We have participated in '3rd Nurse Care Activity recognition Challenge 2021' and proposed a simple machine learning approach to recognize nursing activities. After data pre-processing and feature engineering, we have used several machine learning algorithms. Among them, we have achieved our best results in the Random Forest model. Using this model, We have obtained 72 percent validation accuracy classifying several challenging activities.

关键词： Nurse care activity recognition Smartphone Accelerometer data Class imbalance machine learning Random Forest

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