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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40757 条记录，以下是4281-4290 订阅

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Machine learning Based Intrusion Detection System 3

Machine Learning Based Intrusion Detection System

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Udayakumar, R. Balakrishnan, D. Reddy, Y. Venkatramana Prabhakar, P.B. Edwin Thilaka, A. Kalinga University Department of Cs and It Raipur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India Anurag University Department of It Hyderabad India New Prince Shri Bhavani College Chennai India

ISBN: (纸本)9798350342338

In today's digital landscape, the increasing frequency and sophistication of cyberattacks have highlighted the necessity for robust intrusion detection systems (IDS) to safe- guard sensitive data and critical infrastructure. Traditional rule- based intrusion detection methods have proven to be insufficient in handling the evolving nature of attacks. As a result, machine learning techniques have gained prominence due to their ability to adapt and learn from data, thereby enhancing detection accuracy and efficiency. This paper aims to provide a comprehensive overview of machine learning-based intrusion detection systems, including their significance, challenges, methodologies, and future directions. © 2023 IEEE.

关键词： algorithms cybersecurity Intrusion detection system machine learning

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Non-Invasive BCI by using EMD and Machine learning: A Metaverse Interaction Perspective 20

Non-Invasive BCI by using EMD and Machine Learning: A Metave...

引用

20th international learning and Technology conference, L and T 2023

作者： Ali, Mirna Alsaedi, Nouf Qaisar, Saeed Mian Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia

ISBN: (纸本)9798350300307

People with disabilities struggle to perform specific tasks throughout their daily life. However, BCI systems are developed to assist people struggling with motor impairment by transforming their thoughts into action. Non-invasive BCI systems use electroencephalogram (EEG) to record brain activities. In this study, we segment the EEG signals and then break the segment down into a few intrinsic mode functions using oscillation mode decomposition. Then the intrinsic mode functions are mined for feature extraction. The features mined are processed by different machine learning algorithms for categorization. Among the different algorithms, K-NN yielded the best results with an overall average accuracy score of 95.48%. This approach can be used in future to develop the brain driven metaverse interactive solutions. © 2023 IEEE.

关键词： Intrinsic mode functions

来源：评论

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A Comparative Study of the Stock Market using Machine learning Algorithms 2

A Comparative Study of the Stock Market using Machine Learni...

引用

2nd international conference on Electronics and Renewable Systems, ICEARS 2023

作者： Deevenapalli, Karthik Jampani, Sasi Priyanka Sai, Y. Mohan Venkata Bhagavan Shiva Sudulakunta, Snigdha Chokka, Anuradha Bulla, Suneetha Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350346640

The purpose of this review work is to present a strategy for accurate stock price prediction in the face of multiple factors affecting stock prices. The strategy involves the utilization of four efficient machine learning models - K-Nearest Neighbors (KNN), Naive Bayes, SVM classifiers, and Random Forest classifiers - to analyze and forecast stock values under various market conditions. The performance of the proposed strategy is evaluated through the comparison of accuracy, precision, and recall metrics using a stock price dataset. The intention of this review paper is to provide a comprehensive solution to the challenge of stock prediction by utilizing multiple machine learning algorithms in different scenarios. The framework is then assessed and predicted for its classification accuracy, which will help to overcome the complexities in the stock prediction process and provide a robust and reliable approach to stock price forecasting. © 2023 IEEE.

关键词： Forecasting

来源：评论

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Forecasting Crime Rate Using Artificial Intelligence Applications

Forecasting Crime Rate Using Artificial Intelligence Applica...

引用

2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Srivastava, Rijul Kumar Gupta, Anuj Sharma, Gaurav Chandigarh University Department of Mechatronics Engineering Mohali India Chandigarh University Department of Electronics and Communication Engineering Mohali India

ISBN: (纸本)9798350313987

Crime is a ubiquitous and serious issue in modern society, with a plethora of crimes perpetrated on a daily basis, producing widespread unhappiness. Prevention of Crime is a major critical duty nowadays. Artificial intelligence has recently been an important role that is being noticed practically in every industry, Crime Prediction is also included in that. Comprehensive database has been kept for prior crimes, as this information can be used for future reference. Predicting the chances of future crimes can assist law enforcement organizations in preventing them before they cause the major happening by giving strategic and crucial information based on variables such as time, location and distance. However, the growth rate is considered on the basis of crimes are committed, precisely anticipating crime is a difficult undertaking. As a result, examining good crime rate prediction and analyzing or studying methodologies is critical in detecting and reducing the future crime rate. Multiple academics have introduced studies to predict crimes using various machine learning algorithms and input parameters such as random forest and decision trees. The ultimate goal is to demonstrate the value, need and efficacy of machine learning in predicting violent crimes in a particular region, so that police can use it to lower crime rates and increase societal well-being. © 2023 IEEE.

关键词： Crime Forecasting Machine learning Random Forest Classification

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Analysis of Exoplanet Detection Methods using Machine learning and Deep Neural Networks

Analysis of Exoplanet Detection Methods using Machine Learni...

引用

2023 international conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Singh, Jobanbir Singh, Nongmeikapam Thoiba Chandigarh University Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798350333602

Around 10 sextillion (1021) to a septillion (1024) stars have been estimated to exist in the observable universe. Only 5346 planets outside our solar system have been discovered. Humans are yet to find faster and more advanced techniques to find more earth-like planets. This also requires consistent enhancement of current telescopes and instruments. This study reviews various observational techniques like transits, radial velocities, and spectroscopic analysis. This also includes their associated algorithms like the box-fitting least square (BLS) algorithm, the transit least square (TLS) algorithm, the spectral deconvolution, and machine learning and deep learning techniques like neural networks and the ExoMiner algorithm. This provides a fundamental description of the necessity of these techniques, the fundamental concept behind them, the breakthroughs they have made, and the observational limitations of each method and their accuracies in exoplanet exploration. © 2023 IEEE.

关键词： Astrophysics

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Automated Rat Neuronal Morphology Analysis via Self-Supervised Graph Networks 3

Automated Rat Neuronal Morphology Analysis via Self-Supervis...

引用

3rd international conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Tian, Hao Zhang, Quanbing Anhui University School of Electronic Information Engineering Hefei China

ISBN: (纸本)9798350316674

Neuronal morphology is a key indicator for describing neuronal cell types, analyzing brain developmental processes, and diagnosing changes in neurological diseases. The advancement of neuronal labeling, imaging and reconstruction techniques has greatly expanded neuronal data and promoted neuronal morphology. However, effectively analyzing the extensive and diverse 3D neuronal data remains a considerable challenge. The traditional analysis of neurons mainly relies on manual defined features and visual assessments. Due to the complexity and diversity of neuron data, the above methods are not sufficient to distinguish the fine-grained features in a large number of neurons. In this paper, we address the above problems by introducing neuron morphological characterization algorithm based on graph neural networks through self-supervised learning, which is capable of obtaining a superior neuron characterization. Comparison with the prior methods shows that the algorithm in this paper outperforms the existing supervised algorithms even without annotations. © 2023 IEEE.

关键词： graph neural network neuron classificationn neuron morphology

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Majority Voting Ensemble Approach for Predicting Diabetes Mellitus in Female Patients from Unbalanced Dataset 3

Majority Voting Ensemble Approach for Predicting Diabetes Me...

引用

3rd international conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Muntasir, Fahim Anower, Md. Shamim Nahiduzzaman, Md. Rajshahi University of Engineering and Technology Dept. of Electrical and Electronic Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350345360

Diabetes is a common yet deadly disease among women. In 2017, about one in every nine women were found to be exposed to diabetes in the USA alone. It is needless to say the importance to predict it before it can turn deadlier for the body. In this paper, we used machine learning algorithms to predict the possibility of diabetes in women based on the behavioral and medical diagnosis data related to diabetes. The dataset used here was the Pima Indians Diabetes Dataset by National Institute's of Diabetes and Digestive and Kidney Diseases. The unbalance in the dataset was dealt using synthetic minority oversampling technique (SMOTE). Various machine learning algorithms were opted as base for the prediction. The research work focused on applying hard voting or majority voting ensemble technique on the various combinations of those base algorithms. The aggregation of XGBoost, KNN and Random Forest gave the best performance. According to the classification reports, the precision, recall and, f-1 scores were found to be very good compared to most of the recent and earlier works. The model could predict diabetes with an accuracy of 86%. SHAP analysis was then used to finding out the impact of features on the model performance. © 2023 IEEE.

关键词： Forecasting

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Attention-based Deep learning Model Using Adaptive Margin Loss For Finger-Vein Recognition 3

Attention-based Deep Learning Model Using Adaptive Margin Lo...

引用

3rd IEEE international conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Ke, Liang-Ying Yeh, Shu-Yen Hsia, Chih-Hsien Department of Computer Science and Information Engineering National Llan University Yilan Taiwan Taipei European School Taipei Taiwan

ISBN: (纸本)9798350333862

As the importance of personal and private information continues to increase, biometrics recognition using deep learning (DL) has been attracting more attention. However, traditional finger-vein recognition models have struggled to adapt to the varying quality of images used in everyday life, leading to questions about the model's effectiveness. Because of this, this study proposes the implementation of the CoAtNet model and the adaptive margin loss function (AML) to achieve finger-vein recognition. This method specifically adjusts the difficulty of the training based on different image qualities. The experimental results indicate that the proposed model using AML can have an accuracy rate of 99.80 % in the public dataset FV-USM. © 2023 IEEE.

关键词： Deep learning

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Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

引用

2nd IEEE North Karnataka Subsection Flagship international conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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A Machine learning Approach to Rock and Mine Classification in Sonar Systems Using Logistic Regression 3

A Machine Learning Approach to Rock and Mine Classification ...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Quraishi, Suhail Javed Singh, Malikhan Prasad, Sujit Kumar Arora, Kavita Pathak, Sonal Singh, Anupam School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India BSA College of Engineering & Technology Mathura India Amity University Noida India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350342338

The classification of underwater objects into rocks or mines is a vital task in naval security, marine exploration, and environmental studies. The current work introduces a machine learning-based approach to distinguish between rocks and mines in sonar systems utilizing logistic regression. The novelty of this approach lies in its combination of feature extraction, selection, and a logistic regression model to create an efficient and interpretable binary classifier. The data consists of acoustic signals reflected off underwater objects, which are then processed to identify relevant features. The logistic regression model is trained on a sizeable dataset made up of both hypothetical and actual examples, enabling an objective assessment of performance. The model was refined through a number of trials, which led to an improvement in the model's ability to distinguish between rocks and mines. A comparison of the suggested method with other machine learning methods highlights its effectiveness and simplicity. The study's conclusions have important ramifications for enhancing real-time decision-making in maritime navigation and naval defense. The suggested approach not only improves the precision of separating rocks from mines, but it also paves the way for more investigation into machine learning-based intelligent sonar systems. © 2023 IEEE.

关键词： Logistic Regression Machine learning Naval Security Rock and Mine Classification Sonar Systems

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