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检索条件"任意字段=5th Asia Conference on Machine Learning and Computing, ACMLC 2022"

共 595 条记录，以下是81-90 订阅

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An AI Based Approach for Classification and Performance Evaluation of Teaching Assistant 5

An AI Based Approach for Classification and Performance Eval...

引用

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

作者： Malik, Varun Mittal, Ruchi Singh, Vikram Rana, Ajay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chaudhary Devilal University Sirsa India Engineering and Technology Amity University Uttar Pardesh India

ISBN: (纸本)9798350398267

In order to test and evaluate the performance of various machine learning classifiers for teaching assistant classification, this research will use machine learning approaches. the teaching assistant dataset was taken from the Kaggle data store. After preprocessing the data to remove any inconsistencies, many supervised learning techniques were used, and their efficacy was assessed. the outcomes show that for the teaching assessment models, the ensemble technique performs better than the conventional classification techniques. © 2022 IEEE.

关键词： Supervised learning

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Exploiting Fourier Transform for Multi-scale Electric Load Forecasting 5th

Exploiting Fourier Transform for Multi-scale Electric Load F...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Zhuang, Niangxi Yang, XiaoBing Yang, PeiLin Lian, ChaoHui Sun, LuLu Zhan, ChouJun Nanfang Coll Guangzhou Guangzhou Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Accurate electric load forecasting is one important issue in many applications for power grid systems. However, traditional forecasting models have limitations in capturing nonlinear dynamic properties across different time scales. Hence, the uncertainty and complexity of power grid systems makes accurate prediction of power loads a challenging task. To overcome the bottleneck, we propose a deep learning model called Multi-Scale Fourier Net (MSFNet), which can effectively capture dynamic properties of different scales of power load time series by employing multi-scale analysis through Fourier Transform. Unlike conventional methods, the MSFNet uses FFT to identify primary periodic features as time scales for multi-scale analysis, while also utilizing Gated Recurrent Units (GRU) and the channel attention mechanism to capture long-term dependencies. the PJM electricity market dataset in the United States is adopted to evaluate the performance of the MSFNet. Experimental results indicate that MSFNet outperforms benchmark time series forecasting models in various evaluation metrics, with particularly notable performance in the mean squared error (MSE) and root mean squared error (RMSE) metrics.

关键词： Electric load forecasting Multi-scale analysis GRU Channel attention

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Forecasting Vaccination Growth for COVID-19 using machine learning 5

Forecasting Vaccination Growth for COVID-19 using Machine Le...

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5th International conference on computing and Informatics (IEEE ICCI)

作者： Hartono, Aimee Putri Luhur, Callista Roselynn Qomariyah, Nunung Nurul Bina Nusantara Univ Dept Comp Sci Fac Comp & Media Jakarta 11480 Indonesia

ISBN: (纸本)9781665499736;9781665499729

In this digital era, machine learning (ML) is becoming more common in the healthcare industry. It plays many essential roles in the medical field including clinical forecasting, visualization, and even automated diagnostics. this paper focuses on the future prediction of COVID-19 vaccination rates in different countries. Considering how destructive the novel Coronavirus has been and its continuous mutation and spread, clinical interventions such as vaccines serve as a ray of hope for many individuals. As of 2021, an estimated total of 8,687,201,202 vaccine doses by numerous biopharmaceutical manufacturers have been administered worldwide [1]. this study intends to estimate the probable increase or decrease in global vaccination rates, as well as analyze the correlation between future trends of daily vaccinations and new COVID-19 cases, along with deaths and reproduction rates. three models were utilized in forecasting and comparing the overall prediction toward the COVID19 vaccine rates;Auto-Regressive Integrated Moving Average (ARIMA), an ML approach, Long-Short Term Memory (LSTM), an artificial Recurrent Neural Networks (RNN), and Prophet which is based on an additive model. the Vector Autoregression (VAR) model will also be utilized to compare COVID-19 cases, deaths and reproduction rates to that of COVID-19 vaccine growth. ARIMA resulted to be the best model, while Prophet turned out to be the worst-performing model. In general, our comparison of employing the ARIMA model vs the other three results in the conclusion that adopting this method shows to be a more effective approach in projecting vaccination growth in the future. Furthermore, a visible increase in future daily vaccinations can be seen to be correlated with the increase in COVID-19 cases, deaths reproduction rates, and a fluctuating trend in COVID-19 deaths.

关键词： COVID-19 Vaccination Future prediction Time-series forecasting Evaluation metrics Vaccine distribution machine learning LSTM ARIMA Prophet VAR

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Stocks Price Prediction by Fundamental Analysis Using machine learning Algorithms 5

Stocks Price Prediction by Fundamental Analysis Using Machin...

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

作者： Shende, Shubham Dhananjay Singh, Aniruddha Shrawankumar Shah, Shubham Sushil Shinde, Mayuresh Mohan More, Siddhant Rahul Ainapure, Bharati Vishwakarma University Pune411048 India

ISBN: (纸本)9798350398267

In this academic research, we make an effort to put machine learning methods for stock price prediction. Stock price forecasting uses machine learning effectively. In order to make wiser and more accurate financial decisions, machine learning methods can be examined. the scope of this research work revolves around financial ratios published by companies. this research paper puts forth a stock market price forecasting technique based on the fundamental study of stocks. the technique examines the financial ratios or parameters of stocks over a specific period of time and forecasts whether they will experience a gain or loss. We found that strategies like random forest and the LSTM algorithm function best throughout the system of thinking about various strategies and factors that should be taken into account. © 2022 IEEE.

关键词： Long short-term memory

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machine learning for Fog Data Recovery in Cloud computing 5

Machine Learning for Fog Data Recovery in Cloud Computing

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5th IEEE International conference on Advances in Science and Technology, ICAST 2022

作者： Khandagale, H.P. Halkarnikar, P.P. Tirmare, H.A. Mali, S.A. Shivaji University Department of Technology Maharashtra Kolhapur India D.Y.Patil Institute of Engg. Department of Computer Engineering Akurdi Maharashtra India

ISBN: (纸本)9781665492638

the technology that is frequently utilized for storing a lot of data in any company is cloud computing. A lot of data is produced every day in the data-driven world of today. Data backup and recovery services are required to deal with the unexpected loss of these data. In this paper a multilayer perceptron (MLP) model is designed for predicting the missing data while receiving on cloud based service. the lost data recovery is done by predicting the missing data using machine learning based approach. the multi-layer perceptron model shows good results in terms of RMSE values. © 2022 IEEE.

关键词： Cloud computing

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An Artificial Intelligence Approach for Deploying Zero Trust Architecture (ZTA) 5

An Artificial Intelligence Approach for Deploying Zero Trust...

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5th International conference on computing and Informatics (IEEE ICCI)

作者： Hosney, Eslam Samy Halim, Islam tharwat Abdel Yousef, Ahmed H. Nile Univ Sch Informat Technol & Comp Sci ITCS Giza Egypt Nile Univ Ctr Informat Sci Giza Egypt Ain Shams Univ Fac Engn Cairo Egypt

ISBN: (纸本)9781665499736;9781665499729

Cybersecurity is critical in preventing infractions, maintaining digital workplace discipline, and ensuring that laws and regulations are obeyed. Zero Trust Architecture (ZTA), often known as perimeter-less security, is a novel method for designing and implementing secured IT systems. Zero trust's basic notion is "never trust, always verify," which indicates that devices should not be trusted by default. this means that each access from or to any asset must be assessed and follow the standard guidelines of the organization. Maintaining this type of control imposes a high burden on IT security and system administrators to be able to track and validate each control and manually sustain the configuration needed. With the power of Classification Algorithms in machine learning, we will explore in this paper an alternative solution to save time and effort and help maintain the same security posture with less human intervention. the proposed approach utilizes the information from available security feeds and statically configured policies to enforce and maintain zero-trust network policies. By analyzing the data, it will be feasible to identify the required policies to be configured and compare them against the traditional compliance rules to auto-configure the policies. this approach aims to enhance the existing security intelligence engines with more sophisticated rules and less time and effort.

关键词： Cybersecurity machine learning Zero Trust

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Development of a powerful facial recognition system through an API using ESP32-Cam and Amazon Rekognition service as tools offered by Industry 5.0 2022

Development of a powerful facial recognition system through ...

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5th International conference on machine Vision and Applications (ICMVA)

作者： Estevez Doranres, thelma L. Bertani Hernandez, David Leon Reyes, Arnulfo Miranda Medina, Cynthia E. Tecnol Nacl Mexico Campus Orizaba Orizaba Mexico

ISBN: (纸本)9781450395670

the main intention of this work is to show how the evolution of Industry 5.0 has impacted on the development of highly advanced cloud tools to be integrated into any large-scale technological project. this work describes an Application Program Interface (API) development to take advantage of two major technologies, the use of ESP32-Cam as a means of image acquisition for IoT projects and the artificial intelligence service for facial recognition from Amazon Rekognition as part of the cloud computing and Industry 5.0. this API Integrates both technologies to create a facial recognition and people validation system in the cloud. the end-user system provides a high-speed response latency taking about 5 seconds to identify a person, offering a similarity match greater than 95%.

关键词： ESP32-Cam Amazon Rekognition Deep learning machine learning

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Detection and Classification of Abnormal Data Based on Deep learning and Improved Algorithms 5

Detection and Classification of Abnormal Data Based on Deep ...

引用

5th International conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Li, Ming Wuhan Meteorological Observatory Wuhan China

ISBN: (纸本)9798331527662

Given the rapid growth of emerging systems and technologies, cloud systems of computing, IoT, and other massive data applications, as viewed in this case, are trying to cross thresholds in value realization and real-time analysis. this makes the element of data streams ubiquitous within different industries. Given this, the need for the detection of anomalies within these cohorts of data streams may play a fundamental role in avoiding the risks that come with limiting decision-making. the traditional methods of anomaly and abnormal data algorithms widely relied on acquiring information from a pool of data globally, a factor that made it significantly challenging to streamline and apply the data to different scenarios. However, significant reviews on algorithms within the domain of abnormal or anomaly data detection widely focus on using and exposing data anomalies through learning and improved algorithms, establishing the rationale behind this article. therefore, this article focuses on detecting and classifying abnormal data based on deep learning and improved algorithms in static and different data environments. © 2024 IEEE.

关键词： Adversarial machine learning

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AI Empowered Intrusion Detection for MQTT Networks 5

AI Empowered Intrusion Detection for MQTT Networks

引用

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

作者： Chesney, Steve Roy, Kaushik North Carolina Agr & Tech State Univ Appl Sci & Technol Greensboro NC 27419 USA North Carolina Agr & Tech State Univ Comp Sci Greensboro NC USA

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

this paper utilizes the MQTT IDS dataset to demonstrate how artificial intelligence (AI) algorithms can be used for intrusion detection. the MQTT dataset contains both benign and malicious IoT network traffic that includes both TCP and MQTT protocol-level information. the features within the MQTT IDS dataset are labeled for binary classification of cyberattacks, as various attack types were injected in the configured network to simulate real-world conditions. the dataset consists of five recorded scenarios - normal operations and four attack scenarios: (1) Aggressive scan (Scan A), (2) User Datagram Protocol (UDP) scan (Scan sU), (3) Sparta SSH brute-force (Sparta), and the (4) MQTT brute-force attack (MQTT BF). For this study, both traditional machine learning and deep learning techniques were used to classify attacks with a high level of accuracy. For traditional machine learning, the Random Forest, Logistic Regression, Decision Tree, K-Nearest Neighbors, and the Support Vector machine algorithms were used. For deep learning, a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN) were evaluatedt.

关键词： Internet of things (IoT) machine learning (ML) deep learning (DL) Message Queue Telemetry Transport (MQTT) machine-to-machine (MTM)

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machine learning Model to Predict Mortality Due to Cardiovascular Diseases 5th

Machine Learning Model to Predict Mortality Due to Cardiovas...

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5th International conference on Information Systems and Management Science, ISMS 2022

作者： Bhushan, Megha Haldar, Manimit Dwivedi, Rishi Rajpoot, Tanya Garg, Priyanka Umrao, Shreya School of Computing DIT University Dehradun India

ISBN: (纸本)9783031311529

A popular phrase for conditions affecting the heart or blood arteries is "Cardiovascular Disease". they are the cause for the highest number of deaths worldwide annually. Most of these diseases can be prevented by altering to healthier lifestyles and addressing behavioural risk factors. machine learning plays a significant role in the prediction of cardiovascular diseases, and thus be integral in preventing the resulting deaths. the heart disease dataset was considered from Kaggle. the machine learning models used in this work are, Gaussian Naive Bayes, Support Vector Classifier, Logistic Regression, Decision Tree and Random Forest Classifier, K-Nearest Neighbours Classifier, with the highest accuracy achieved being 96% by Stacking Classifier model. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Logistic regression

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