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检索条件"主题词=Machine Learning Algorithms"

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AIGC German Vocabulary Memory Optimization Algorithm Based on machine learning

AIGC German Vocabulary Memory Optimization Algorithm Based o...

引用

Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Bo Wang Xi'an Fanyi University Xi'an Shaanxi China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Traditional vocabulary learning methods often lack individuality and interest, and it is difficult to meet the diverse needs of learners. Therefore, this study proposes an innovative algorithm framework, which combines ML algorithm and AIGC (Artificial Intelligence Generated Content) technology, and can intelligently recommend and generate individualized vocabulary learning content according to learners' individual characteristics and learning progress. In terms of methods, this article constructs an algorithm framework including four core parts: data input, analysis and processing, model decision-making and content output, and realizes the individualized function of the algorithm through key steps such as feature selection, data preprocessing, model construction and training, and AIGC content generation mechanism. The experimental design adopts the comparative research method to compare the learners who use the traditional vocabulary memory method with those who use the algorithm to verify the effectiveness of the algorithm. The experimental results show that the learners who use the algorithm have obvious advantages in vocabulary memory efficiency, learning interest and long-term memory. The conclusion of this article provides new ideas and methods for German vocabulary teaching, which has important practical significance and application prospects.

关键词： Training Vocabulary Analytical models machine learning algorithms Education Memory management machine learning learning (artificial intelligence) Data models Optimization

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Mental Health Diagnostics Using machine learning: A Clustering and Predictive Modeling Approach

Mental Health Diagnostics Using Machine Learning: A Clusteri...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ritu Chauhan Aarushi Mishra Mehak Jena Eiad Yafi Megat F. Zuhairi Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The application of machine learning has rapidly advanced as a transformative approach in healthcare, offering significant potential in the analysis of huge amounts of acquired data on Mental health across the world enhancing the treatment of mental health issues. machine learning algorithms are being utilized to analyse patterns and trends in mental health datasets. This study investigates various machine learning methodologies and descriptive analytics techniques to detect patterns and trends in mental health-related factors, focusing on predicting the likelihood of individuals seeking mental health treatment. We utilized clustering, association rule mining, feature selection, and machine learning classifiers such as Random Forest, Decision Tree, XGBoost, and Logistic Regression. By applying these techniques to a dataset of mental health factors, we identified key predictors influencing mental health outcomes and developed models to enhance predictive accuracy. Our analysis highlighted the effectiveness of machine learning algorithms in extracting meaningful patterns from the dataset, with Random Forest and XGBoost emerging as particularly strong performers with accuracies of 81% and 84%. Other applied algorithms that are Decision Tree, Gradient Boosting and logistic regression have the accuracy of 81%, 77%, and 73% respectively.

关键词： Logistic regression machine learning algorithms Accuracy Data analysis Mental health Predictive models Market research Decision trees Public healthcare Random forests

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Leveraging machine learning for Predicting Molecular Refractivity in Drug Toxicology

Leveraging Machine Learning for Predicting Molecular Refract...

引用

International Conference on Signal Processing and Communication (ICSC)

作者： Saloni Joshi Reema Gabrani Ruby Beniwal Dept. of Biotechnology Jaypee Institute of Information Technology Noida UP India Dept. of Electronics and Communication Jaypee Institute of Information Technology Noida UP India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The integration of electronics engineering into molecular refractivity prediction is transforming drug toxicology studies. By combining biosensors, machine learning algorithms, and high-speed data systems, scientists can better predict toxicological profiles of drugs. This interdisciplinary approach improves safety and efficacy in drug development. Electronics engineering enables real-time monitoring of drug metabolism through wearable devices and predictive models, identifying toxic metabolites early and reducing adverse reactions. These technologies enhance the assessment of metabolic processes, guiding safer drug development. machine learning algorithms further refine predictions by analyzing large datasets, identifying toxicity patterns, and improving personalized treatment plans, ultimately reducing time and costs in toxicology testing. In this research work, machine learning classifier accuracy has been achieved 85% to identify toxic drugs and toxicity prediction accuracy by using the Bayesian Neural Network was 95.8%.

关键词： Drugs Toxicology machine learning algorithms Accuracy Refractive index machine learning Predictive models Real-time systems Safety Metabolism

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Privacy Preserving Skellam Mixture Model Fusion for Robust Statistical learning

Privacy Preserving Skellam Mixture Model Fusion for Robust S...

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International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Chitra M. Tvisha Prasad Anshuman Suresh Department of Electronics and Communication Engineering Ramaiah Institute of Technology Bangalore India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

In an era of extensive data collection, preserving individual privacy while deriving actionable insights is a critical challenge. This study proposes a Privacy-Preserving Skellam Mixture Matrix Fusion (PPS MMF) framework enhanced by a Bidirectional Encoder Representations from Transformers (BERT) model. The PPS MMF leverages the Skellam distribution to obscure sensitive information during data fusion, ensuring privacy preservation. By integrating BERT, the framework captures nuanced contextual information, improving the accuracy of downstream tasks. Operating in a decentralized manner, this approach mitigates centralized data breach risks and enables secure data fusion across domains like healthcare, finance, and social media. Experimental results validate its effectiveness and practicality in real-world applications.

关键词： Wireless communication Data privacy Accuracy machine learning algorithms Federated learning Signal processing algorithms Data integration Bidirectional control Encoding Data models

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Enhancing Consumer Electronics Security in Smart Homes with t-SNE Feature Selection and Seagull Optimized Deep learning Model

Enhancing Consumer Electronics Security in Smart Homes with ...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Akshat Gaurav Brij B. Gupta Kwok Tai Chui Ronin Institute Montclair NJ USA Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan & University of Economics and Human Science Warsaw Poland Hong Kong Metropolitan University (HKMU) Hong Kong

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This work presents a t-SNE-based feature selection and Seagull Optimized Deep learning Model to improve consumer electronics security in smart homes. Optimized using the Seagull Optimization Algorithm (SOA), the suggested Convolutional Neural Network (CNN) model showed exceptional performance with an accuracy of 92% in identifying cyber risks. The model routinely exceeded in both accuracy and loss measures when tested against conventional machine learning and deep learning models. T-SNE helped to choose important criteria that provide strong identification of harmful activity. Our method presents a major development in smart home security and a dependable instrument for reducing cybersecurity threats in a linked world.

关键词： Deep learning Accuracy machine learning algorithms Smart homes Feature extraction Loss measurement Convolutional neural networks Computer security Consumer electronics Optimization

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Incident-Angle-Insensitive Dual-Polarized Metacell Using machine-learning-Based Optimization

Incident-Angle-Insensitive Dual-Polarized Metacell Using Mac...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Peiqin Liu Yuqing Zhou Zhi Ning Chen Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this paper, an incident-angle-insensitive, dual-polarization metacell is proposed using a machine-Iearning-based optimization method. The metacell is based on a three-layer dual-polarized metacell with two substrates vertically and symmetrically integrated, which enhances transmission performance across a wide range of incident angles for both TE and TM waves. A seven-layer artificial neural network (ANN) model is employed to optimize the metacell dimensions and achieve the desired performance. The input data for the ANN consists of the target transmission responses and the incident angle. The neural network predicts the geometric parameters that meet the specified performance criteria. As a result, a dual-polarized metacell is optimized to exhibit incident-angle insensitivity up to 60° for both TE and TM incident waves.

关键词： machine learning algorithms Correlation Optimization methods Europe Artificial neural networks machine learning Prediction algorithms Metamaterials Electromagnetics Substrates

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A Proficient Approach in Crop Recommendation System using Gradient Boosting machine Technique 3

A Proficient Approach in Crop Recommendation System using Gr...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kumar, Kandukuri Joseph Reddy, N Praneeth Kumar Vasu, Andhavarapu Sandosh, S. Business Analytics Vellore Institute of Technology Integrated Mtech in CSE Specialization Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

The Crop Recommendation System (CRS), a powerful data-driven tool, has revolutionized farming practices by delivering precise crop recommendations. Decision Trees and Logistic Regression are employed as initial models to assess crop choices and further enhance accuracy with Gradient Boosting machine (GBM), which consistently outperforms other methods. CRS meticulously processes essential factors, including Nitrogen (N), Phosphorus (P), Potassium (K), Temperature, Humidity, Rainfall, and pH levels. These inputs lead to highly personalized recommendations, tailored to specific farming contexts. CRS significantly boosts agricultural productivity, providing farmers with insights for optimal crop selection, increased yields, and efficient resource allocation. It promotes sustainability by encouraging environmentally responsible farming practices. In modern agriculture, CRS emerges as a pivotal asset, contributing to productivity and global food security. Its ability to guide farmers toward sustainable practices positions it as a driving force for positive change in the agricultural sector, offering a path toward a more sustainable and food-secure future. © 2023 IEEE.

关键词： Agricultural Decision Support System Crop Modeling Data-driven Agriculture machine learning algorithms Precision Farming Remote Sensing Technologies

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Enhanced Quality of learning (QOL) via Secured Recommendation and Sentimental Analysis in Online Classes

Enhanced Quality of Learning (QOL) via Secured Recommendatio...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： T. Saravanan S. Saravanakumar V Ganesh Karthikeyan Dept. of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Dept. of CSE Faculty of Engineering &Technology JAIN (Deemed to be University) Bengaluru India School of Computing SASTRA Deemed University Thanjavur India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Nowadays online classes have become the main source of students learning. To achieve this, students' interaction is required. Basically, Cloud can provide more learning and interaction among the students and teachers (faculty). If teachers are good students are creating problem, if students are good and teachers are not so. To sort out all these things, complete monitoring is required. In case if a proper recommendation with guidance is provided then it will be easy for student to cope up and will be easy for the teachers to validate the students' progress. The proposed system provides a deep learning Recommender System knowing the capability of the students. Here, the quality of learning is enhanced by predicting the student/teacher's emotion via sentimental analysis. Recommender system is best for this. The proposed system also uses LSTM (Long Short-Term Memory Algorithm) to predict the students' activities and prefer recommendation according to their progress. This system helps student for developing their career and notice the teachers' activities for controlling the stress level of the students. By this way the students' creativity will increase and stress will be reduced. The system further provides more security with proper experimental analysis.

关键词： Deep learning machine learning algorithms Engineering profession Anxiety disorders Prediction algorithms Security Long short term memory Recommender systems Monitoring Creativity

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Random Forest based machine learning Algorithm for Estimating State of Charge in Lithium-ion Batteries

Random Forest based Machine Learning Algorithm for Estimatin...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： D S R S L Avanthika Bhamidi Lokeshgupta Ramanjaneya Reddy Udumula Electrical and Electronics Engineering SRM University AP Guntur India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

This paper proposes a Random Forest (RF) machine learning algorithm-based prediction model for the state of charge (SoC) level of lithium-ion batteries for electric vehicles. To show the effectiveness of the proposed prediction model performance, the RF model has been compared with the other machine learning algorithms such as Support Vector machines (SVM) and Gradient Boosting (GB) approaches. The dataset includes cell temperature, state of charge (SoC), voltage, and current readings at three different external temperatures—15, 25, and 30 degrees Celsius are considered in this paper to test the performances of the proposed model. After preprocessing of the dataset, 20% of the data was used for testing and the remaining 80% for training purposes. The various metrics such as mean squared error (MSE), mean absolute error (MAE), coefficient of determination $\left(R^machine learning algorithms\right)$, root mean squared error (RMSE), normalized root mean squared error (NRMSE), residual standard error (RSE), and relative absolute error (RAE) are usually preferred to evaluate the performance of the prediction models. The simulation results of the proposed model clearly show the effectiveness of SoClevel estimation for real-time battery management systems (BMS) when compared to other machine learning algorithms. The efficiency of the proposed model is $\mathbf{9 9 \%}$ and execution time is less than 5 seconds. The accurate estimation of the SOC of lithium-ion batteries is crucial for optimizing battery performance, ensuring safety, and extending battery life in electric vehicles.

关键词： Support vector machines Radio frequency Lithium-ion batteries Measurement machine learning algorithms Computational modeling Simulation Predictive models State of charge Random forests

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machine learning Approach for Detection of Harassment and Hate Speech in Sinhala Text Messages on Telegram

Machine Learning Approach for Detection of Harassment and Ha...

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Advances in Technology and Computing (ICATC), International Conference on

作者： Saumya Cooray Irani Anjana Sandaru Thalagahawaththa Udani Jayakody Department of Information and Communication Technology Faculty of Technology Rajarata University of Sri Lanka Sri Lanka

ISBN: (数字)9798331518219

ISBN: (纸本)9798331518226

Nowadays, social media platforms have become an important communication method to connect and express messages without geographical limitations. Nevertheless, harassment and hate speech are some of the main negative outcomes of these platforms, poisoning digital spaces online and leading to depression and even suicide. Hate speech and harassment target individuals or groups based on attributes such as gender, religion, race, or sexuality. According to Sri Lanka CERT (National Centre for Cyber Security), hate crime incidents rose 38% in 2021, with 182 reported cases against 70 in 2020. Although, to overcome that concern, specifically in Sinhala text messages on Telegram, there is a lack of relevant studies. Consequently, this study aims to develop a machine learning model to efficiently identify and mitigate a spectrum of harassment and hate speech, within Sinhala Telegram text messages. The initial step involved developing a Telegram API and a Python application using the Telethon library to collect Telegram text messages automatically. Telegram messages were manually annotated into two categories based on different factors. Sinhala language specific data preprocessing NLP techniques such as stemming, and tokenization were utilized for normalization and feature extraction. A vectorizer was utilized to convert textual data into a format that can be understood by a classifier. The study found the Support Vector machine classifier to be effective since, when compared to other developed machine learning classifiers such as Random Forest, Logistic Regression, Decision Tree, and Linear SVC, it has an F1-Score of 0.87. All these models exhibited success to varying degrees and were tested on a balanced dataset.

关键词： Support vector machines machine learning algorithms Prevention and mitigation Hate speech Static VAr compensators machine learning Feature extraction Tokenization Numerical models Random forests

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