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共 595 条记录，以下是181-190 订阅

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Using EEG Nonlinear Dynamic Features and machine learning to Identify Different Organizational Commitment 5

Using EEG Nonlinear Dynamic Features and Machine Learning to...

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5th International conference on Data Science and Information Technology, DSIT 2022

作者： Zhang, Rui Wang, Zi-Yang Liu, Yu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

As machine learning has greatly improved the depth and width of EEG analysis and psychological research, it has become possible to analyze the relatively stable personalities of individuals based on EEG. In this paper, we recorded the restingstate EEG of subjects, labeled them using their score of organizational commitment, and achieved automatic recognition. In order to complete such a new challenging classification task, we extracted a variety of different nonlinear dynamic features from resting-state EEG, and then used different machine learning models (SVM, GBDT, KNN, LR, Gaussian NB) to classify these features, and next evaluated experimental results based on crossvalidation. the results show that Permutation Entropy and Approximate Entropy achieved best accuracy, which both obtained an overall accuracy of more than 70% based on machine learning. Furthermore, we adopted a stacking strategy and constructed a fusion model to improve the performance. the experimental results show that using the Stacking model to classify the Permutation Entropy of EEG can achieve an overall accuracy of 82.6% with 83.3% recall and 0.827 F1-score. In addition, we also conducted a comparative analysis of EEG signals with different lengths of sample and compare eyes-open and eyes-closed EEG. © 2022 IEEE.

关键词： Entropy

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Real-Time Monitoring of Video Quality in a DASH-based Digital Video Broadcasting using Deep learning 5

Real-Time Monitoring of Video Quality in a DASH-based Digita...

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5th International conference on Artificial Intelligence, Big Data, computing and Data Communication Systems (ICABCD)

作者： Motaung, William Ogudo, Kingsley A. Chabalala, Chabalala Univ Johannesburg Dept Elect & Elect Engn Johannesburg South Africa Univ Witwatersrand Sch Elect & Informat Engn Johannesburg South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Digital video processing and transmission can introduce numerous distortions while capturing signals from broadcasting stations. these distortions become a nightmare for multimedia companies, especially terrestrial broadcasting companies that have fully adopted the online video streaming service. While terrestrial broadcasting benefits from online streaming through over-the-top (OTT) channels, there is a potential setback to reducing the video quality due to preprocessing of signals. Video quality assessment (VQA) algorithms have been developed for analyzing the quality of videos in a database, but little attention has been paid to implementing such algorithms in a real-time situation. this paper develops a novel real-time VQA framework by integrating a deep learning technology into the broadcasting pipeline. Previous studies used objective metrics augmented with subjective values to validate techniques. However, this approach is not appropriate for real-time video evaluation. Our proposed framework uses objective metrics (devoid of subjective scores like mean opinion scores) but rather introduced a new metric to validate the framework. the whole framework is validated using compressed/uncompressed signals and varying devices to show the signal differences. Results show that the framework is a step toward feasible incorporation of a VQA tool in a digital terrestrial television model. Using 100 epochs for our simulated video stream, the restricted Boltzmann machine yields a root mean square and mean absolute of 3.6903 and 2.3861 respectively.

关键词： deep learning digital video broadcasting multimedia streaming restricted Boltzmann machine video quality assessment

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Crop Fertilizer Prediction using Regression analysis and machine learning algorithms 5

Crop Fertilizer Prediction using Regression analysis and Mac...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Monika, K. Ramprakash, Balakrishnan Muthuramalingam, Sankayya Mirdula, K. Thiagarajar College of Engineering Department of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Department of Information Technology Madurai India

ISBN: (纸本)9798350398267

India is one among the well-known countries whose socio-economic status is dates by agriculture. the rise of sedentary human civilization is the key role achieved in the development of agricultural practice. the problems in agriculture revolves not only about the productivity of the crop but also the amount of fertilizer, herbicide, insecticide used and also include the amount of water that are required to get a good productivity to the farmers. machine learning is used in agriculture to find solutions to the problems with high accuracy for the benefit of the farmers by recommending fertilizers, pesticides, etc., according to their needs. there are several machine learning algorithms that are widely used in our day-today life in various domains. this paper gives a detailed description of predicting the suitable variety of fertilizer based on the machine learning technique like multiple linear regression and lasso regression as it takes the relationship between the dependent and independent variables that affects the resultant prediction. It gives a brief overview for recommending suitable fertilizer for the conditions given by the farmers like temperature, humidity, soil type, crop type, quantity of fertilizers like nitrogen, potassium, phosphorus that are present in the soil, etc., this paper implies that the lasso regression is the most suitable regression algorithm for predicting the suitable variety of fertilizer for the climatic and field conditions given by the farmers with an accuracy of 89%. the developed model helps the farmers to find the most suitable fertilizer with respect to the soil and climatic conditions of the field to have higher yield, which in turn increases the profit for the farmers. the application of the suitable variety of fertilizer to the soil also helps to increases the nutrient contents that are present in the soil. © 2022 IEEE.

关键词： Crops

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Software Complexity Prediction Model: A Combined machine learning Approach 5th

Software Complexity Prediction Model: A Combined Machine Lea...

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5th International conference on Recent Innovations in computing , ICRIC 2022

作者： Birihanu, Ermiyas Adamu, Birtukan Kefie, Hailemichael Beshah, Tibebe Software Engineering Wolkite University Wolkite Ethiopia Applied Informatics Silesian University of Technology Gliwice Poland School of Information System Addis Ababa University Addis Ababa Ethiopia

ISBN: (纸本)9789819906000

the need for computers increased quickly. As a result, the program is utilized in a significant and intricate manner. More complex systems are being developed by software businesses. Additionally, customers expect great quality, but the market requires them to finish their assignment faster. Different measuring methods are employed by software firms. Some of these include customer feedback after it has been given to customers, software testing, and stakeholder input. the objective of this project is to use a combination of machine learning techniques to predict software bug states using the NASA MDP dataset. the research process considered data preprocessing methods and applied singular and combination machine learning algorithms. To create the model, the single classifiers were combined using the voting method. Accuracy, precision, and recall were used to evaluate the model's effectiveness, along with tenfold cross-validation. the promising result was recorded by a combination of J48 and SMO classifiers. Before attempting to test the software product, the researcher retrieved attribute data from the source code;the complexity of the software product will then be ascertained using the constructed model. the main contribution of this study is to improve software quality by incorporating a machine learning framework into the present software development life cycle between implementation and testing. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Enhanced machine learning Model for Design and Evaluation of Various Algorithms Applied in Clinical Cardiovascular Disease 5

Enhanced Machine Learning Model for Design and Evaluation of...

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5th International conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Rai, Aryan Damodharan, D. Singh, Ajay Shanker Anandhan, K. Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

Cardiovascular Diseases (CVD) are the category of diseases mainly involving the heart and the blood vessels of our body. It is one of the most challenging obstacles to combat for the healthcare sector. the early prediction of Cardiovascular Diseases is essential in saving human lives from these diseases. Many methods exist for reducing risk factors such as blood pressure and cholesterol levels. In our research work, we are considering 4133 range of index as an input dataset. the machine learning algorithm is a prominent technology to predict diseases at earlier stages. the various machine learning algorithms are implemented in our research work such as KNN, SVM, Kernel SVM, Decision Tree, Random Forest, Naïve Bayes, XGBoost. Due to the Covid 19 pandemic, people around the world were forced to quarantine at home for long periods, encouraging a rise in parameters such as BMI Hypertension, improper nutrition, diets, sodium, lack of exercise, high cholesterol, etc. these factors have dramatically increased by 62% from 2019 to 2022. Our implementation results shows logistic regression - 85.39%, KNN - 84.42%, SVM-84.71%, Kernel SVM-84.81%, Naïve Bayes - 83.17%, Decision Tree - 77.17%, Random Forest 85%, and XGBoost-85.49% respectively. the overall highest accuracy out of all these algorithms is of XGBoost, at 85.49%. © 2022 IEEE.

关键词： Decision trees

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A Survey of machine learning for Assessing and Estimating Student Performance 5th

A Survey of Machine Learning for Assessing and Estimating St...

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5th International conference on Recent Innovations in computing, ICRIC 2022

作者： Kaur, Avneet Bhatia, Munish Department of Computer Science and Engineering Lovely Professional University Punjab India Department of Computer Applications NIT Kurukshetra Kurukshetra India

ISBN: (纸本)9789811998751

Educational data mining (EDM) contributes cutting-edge methodologies, strategies, and applications to the advancement of the education system, hence playing a crucial part in its development. Utilising machine learning and data mining approaches to explore and utilise educational data, the current advancement gives essential tools for comprehending the student learning environment. Academic institutions in the twenty-first century operate in a highly competitive and complicated environment. Among the prevalent issues faced by universities are performance analysis, the provision of a high-quality education, systems for evaluating the performance of students, and the planning of future activities. Student intervention programmes must be created in these universities in order to address the academic difficulties encountered by students. From 2009 through 2021, the relevant EDM literature relative to predicting student attrition and students at risk is examined in this review. According to the review’s results, several machine learning (ML) methodologies are used to discover and address the fundamental challenges of forecasting students at risk and student withdrawal rate. Furthermore, the bulk of studies make use of data from student college/university database and online learning portals. It was determined that ML techniques play crucial roles in forecasting students at risk and withdrawal rates, hence boosting student performance. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Malicious and Benign URL Dataset Generation Using Character-Level LSTM Models 5

Malicious and Benign URL Dataset Generation Using Character-...

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5th IEEE conference on Dependable and Secure computing (IEEE DSC)

作者： Vecile, Spencer Lacroix, Kyle Grolinger, Katarina Samarabandu, Jagath Western Univ Dept Elect & Comp Engn London ON Canada

ISBN: (数字)9781665421416

ISBN: (纸本)9781665421416

As technologies advance, so do the attacks on them. Cybersecurity plays a significant role in society to protect everyone. Malicious URLs are links designed to promote scams, attacks, and frauds. Companies often have web filtering algorithms that will blacklist specific URLs as malicious;however, due to privacy concerns, they will not give outside entities access to their cybersecurity data. Unfortunately, this lack of data creates a dire need for more data in cybersecurity research and machine learning applications. this paper proposes using machine learning to generate new synthetic URLs characteristically indistinguishable from the data they replace. To do this two character-level long short-term memory (LSTM) models were trained, one to generate malicious URLs and one to generate benign URLs. To assess the quality of the synthetic data two tests were performed. (1) Classify the URLs into malicious and benign to ensure the characteristics of the original data were preserved. (2) Use the Levenstein ratio to check the similarity between the real and synthetic URLs to ensure sufficient anonymization. the results from the classification test show that the synthetic data classifier only slightly underperformed the real data classifier;however, with having accuracy, precision, recall, sensitivity, and specificity above 99%, it can be concluded that the characteristics of the malicious and benign URLs were preserved. the Levenstein ratio tests showed a mean of 67% and 79% similarity for the benign and malicious URLs, respectively. In the end, the character-level LSTM model successfully generated an anonymized, synthetic dataset, that was characteristically similar to the original, which could pave the way for the publication of many more datasets in this way.

关键词： LSTM Deep learning Dataset Generation Dataset Anonymization Malicious URLs Cybersecurity

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Manufacturing Data Privacy Protection System for Secure Predictive Maintenance 5

Manufacturing Data Privacy Protection System for Secure Pred...

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5th International conference on Data Science and Information Technology, DSIT 2022

作者： Ao, Yuan Jiang, Yuchen State Key Laboratory of Information Security Institution of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China University of Nebraska Lincoln College of Engineering School of Computing Nebraska United States

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

In Industry 4.0 technology of industrial equipment is becoming more and more complex as physical-digital systems are combined to form a new industrial phase. the complexity of the equipment affects the frequency of equipment failures, and to avoid failures we need to predict the health of the equipment. We found that PdM is the most effective health prediction, but it has the challenges of data security and data availability. To address this aspect, we design and develop a manufacturing data privacy protection system (MDPPS). Data are collected by smart sensors and then encrypted. After that, machine learning models are used to extract features and predict the remaining useful life of the industrial equipment. Finally, the predicted results will help workers to carry out maintenance activities. the performances of the MDPPS achieve an average of 87.40% Accuracy, 95.57% Precision, 77.74% Recall, and 83.86% F1-Score. © 2022 IEEE.

关键词： machine learning

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5th International conference on Advanced Informatics for computing Research, ICAICR 2021

5th International Conference on Advanced Informatics for Com...

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5th International conference on Advanced Informatics for computing Research, ICAICR 2021

ISBN: (纸本)9783031094682

the proceedings contain 23 papers. the special focus in this conference is on Advanced Informatics for computing Research. the topics include: Efficient Entity Resolution for Bibliographic Data Using MapReduce;securing of Identification System Data Transmission Using Deep Autoencoders and Data Hiding;a Hybrid Filter/Wrapper machine learning Model for Classification Cancer Dataset;edge-Assisted IoT Architecture: A Case of Air Pollution Monitoring Frameworks;optimized Analysis Using Feature Selection Techniques for Drug Discovery Detection;crop Identification and Disease Detection by Using Convolutional Neural Networks;an Efficient Novel Approach for Detection of Handwritten Numericals Using machine learning Paradigms;multiclass Classification in machine learning Algorithms for Disease Prediction;CREA-Components Reusability Evaluation and Assessment: An Algorithmic Perspective;capsule Network Based Speech Emotion Recognition for Efficient Capturing of Spatial Features;a Survey and Analysis on the Distraction Patterns of the Students in E-learning and M-learning Environments;relationships Among Human Genome Graph Elements Using Clusters Detection;transfer learning Architecture Approach for Smart Transportation System;preface;a Blockchain-Based Solution for Electronic Medical Records System in Healthcare;analysing Sentiments of People Over Vaccines in Reddit Posts Using Natural Language Processing;segmentation of Tumor Region from Mammogram Images Using Deep learning Approach;review on IoT Based Real-Time Healthcare Monitoring System;application of IoT in 5G Wireless Communication: A Detailed Review;enhanced Service Point Approach for Microservices Based Applications Using machine learning Techniques;Worldwide Vaccination Report for COVID-19 Analysis and Visualization Using Deep learning.

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VIPER: A Virtual Platform to Experience Robots 5

VIPER: A Virtual Platform to Experience Robots

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5th IEEE International conference on Artificial Intelligence and Virtual Reality (IEEE AIVR)

作者： Arntz, Alexander Di Dia, Agostino Riebner, Tim Strassmann, Carolin Voelker, Stefanie Eimler, Sabrina C. Univ Appl Sci Ruhr West Inst Comp Sci Bottrop Germany Univ Appl Sci Ruhr West Inst Engn Mech Mulheim Germany

ISBN: (纸本)9781665457255

We present a virtual reality (VR)-based learning platform that allows the exploration and experimentation with industrial colloborative robot manipulators. Students can immersive themselves into virtual scenarios with robots' that are otherwise difficult to realize in physical classroom environments. Behavior and functionality can be explored comprehensively. For this purpose, the VR learning application is equipped with a variety of interaction mechanics that enable the portrayal of a range of different tasks. A network integration allows for multiple users to be present in the virtual environment simultaneously. Additionally the VR application contains a library of different industrial robots that can either act autonomously controlled by machine learning agents or be controlled based on the kinematics-based simulation. the application is specifically designed to serve as a learning tool that can be iteratively extended to fit the requirements of different robotics-related courses.

关键词： learning Application Virtual Reality machine learning Robots

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