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Implementing Newly Proposed Dynamic Tree Structure: Development, Integration, and Optimization

Implementing Newly Proposed Dynamic Tree Structure: Developm...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Kumari, Sweety Kumar, Biresh Thakur, Kanika Gaurav, Kumar Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India

ISBN: (纸本)9798350385199

An innovative data structure called a 'dynamic tree' has important uses in algorithmic time complexity optimization. It creates well-organized trees by modifying the properties of several single-node trees, therefore preserving the forest structure. This structure is essential to improving the effectiveness of many algorithms. In this work, I particularly address the 'Maximum Flow method,' a crucial subtopic in the larger field of dynamic trees. One basic method for solving network flow issues is the Maximum Flow Algorithm, but it frequently runs into issues with time complexity. My research attempts to overcome these obstacles by using dynamic trees to enhance the efficiency of the algorithm. This work reduces the time complexity of network flow problems, leading to more effective solutions. The findings have potential implications in a variety of computing and real-world scenarios. © 2024 IEEE.

关键词： Ancestor Cut Evert Find Key Link Root Top tree

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Self-supervised Federated Learning for Anomaly Detection

Self-supervised Federated Learning for Anomaly Detection

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Eng, Tze-Qian Pao, Hsing-Kuo Liao, Chi-Chen National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Federated learning-based strategy can confirm data privacy, as well as other related ones like split learning and differential privacy. When enjoying secured data sharing, we also worry that the data privacy preserving may harm the model's overall performance. Given the most recent progress in machine learning and AI methodology, we incorporate self-supervised learning to plug in the federated learning framework and the integrated system can guarantee the model performance and data privacy preservation simultaneously. In the integrated framework, we have different clients to keep their own data, and the data are well separated into the attribute half and the label half, for enhanced privacy, not to mention the additional privacy-preserving skill like differential privacy. Given all the aforementioned components, we can still have the privacy-preserving components equipped model performed superior to or is compatible with the performance without the privacy-preserving components. That is, one client can perform better when adding more information from other clients, without the data sharing in between, and no clients own both the attributes and the corresponding labels. The focused topic is anomaly detection and we pay attention to the imbalanced nature of the data which shows additional challenges to the problem. Given the problem, self-supervised learning is especially useful when obtaining the label information is considered non-trivial. After all, we demonstrate the overall model effectiveness when compared to methods without any federated learning components. © 2023 IEEE.

关键词： Anomaly detection differential privacy federated learning self-supervised learning split learning

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A Case Study of Detecting HULK-based DDoS Traffic with Random Forest Classifier

A Case Study of Detecting HULK-based DDoS Traffic with Rando...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chang, Tsu-Chen Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Modern network administration desires to have early detection of DDoS traffic before damages occur. Nevertheless, as Internet traffic grows over years, it becomes more challenging to detect DDoS traffic in an efficient and effective manner. The survey of existing literature shows that random forest classifier, when applied to DDoS detection, yields great performance at low learning costs. This work is devoted to a case study of implementing and using random forest classifier to detect DDoS traffic generated by a well-known DDoS tool named HULK. Our result indicates that when used with a good feature selection mechanism, random forest classifier can achieve a high detection accuracy with fast training time. © 2023 IEEE.

关键词： HTTP

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An Implementation of Feature Selection for Detecting LOIC-based DDoS Attack

An Implementation of Feature Selection for Detecting LOIC-ba...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chen, Shih-Chieh Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Machine learning method is efficient and effective in detecting DDoS attacks, but it all begins from identifying and selecting their important features. This paper presents an implementation of feature selection for DDoS detection based on Random Forest method. In our implementation, we use a LOIC software flood DDoS requests to a target computer, then control the target to extract the features from the captured IP packets, and finally calculate their Gini feature importance and ranking for subsequent feature selection. © 2023 IEEE.

关键词： Feature Selection

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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

引用

16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization 5

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Majumder, Sourav Singh, Dharmpal JIS College of Engineering Dept. of Information Technology Kalyani India Swami Vivekananda University Dept. of Comouter Science Kolkata India Capgemini Kolkata India JIS College of Engineering Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350395235

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses. © 2024 IEEE.

关键词： Support vector machines

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Improving Liver Disease Diagnosis Through Generative Adversarial Network Driven Synthetic Tabular Data

Improving Liver Disease Diagnosis Through Generative Adversa...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Mahendran, S. Venkatasekhar, D. Shanmugasundaram, G. Annamalai University Department of Information Technology Chidambaram India Chennai Institute of Technology Dept. of Computer Science & Engineering Chennai India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540821

Liver ailments pose a significant worldwide health concern, with their extensive effects and severe repercussions highlighting the pressing necessity for precise diagnosis. Diagnosing hepatic disorders is frequently intricate and expensive. This work presents a Generative Adversarial Network (GAN) based methodology for producing synthetic tabular data to enhance early prediction of liver ailments, employing a dataset of Indian liver patients. The contribution of the work is the introduction of innovative architecture that integrates Stacked Ensemble Model with advanced preprocessing techniques and the use of Generative Adversarial Networks for data augmentation not extensively explored in current data-driven machine learning approaches for liver ailments prediction. The Stacked Ensemble Model has two tiers: the base-level and the meta-level. The base-level learning component trains various base learners, including Gradient Boosting, Ride classifier, Multi-layer Perceptron, and LightGBM on the input dataset. A strong prediction model is then constructed by combining and feeding the outputs of these several classifiers into a Random Forest classifier, which is the meta-learner. The results show that the model performs much better when synthetic data generated by GANs is included, obtaining an overall accuracy of 95%. This approach aims to greatly improve the prediction of liver ailments, hence enabling quick intervention and more effective treatment. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Hyperspectral Image Compression Through Stacked Autoencoder Approach 6

Enhancing Hyperspectral Image Compression Through Stacked Au...

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6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Afrin, Afsana Haque, Md. Rakibul Mamun, Md. Al Inst. of Information and Communication Technology Rajshahi University of Engineering Technology Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

Hyperspectral Image (HSI) consisting of numerous high-resolution spectral bands creates challenges of the high dimensionality problem in HSI classification which hinder real-life applications despite their abundance of information. We present a lossy compression approach using stacked autoencoders to reduce high dimensionality problem in this paper. The pro-posed method utilizes stacked autoencoders to extract features from HSIs, allowing compression and subsequent reconstruction. The study demonstrates improved Peak Signal-to-Noise Ratio (PSNR) of 70.43, 60.87 and 61.36 on three distinct HSI datasets-Salinas, Botswana, and KSC respectively compared to previous autoencoder-based compression method. Additionally, the impact of compression on classification accuracy is assessed using a 3D-2D CNN model, achieving an average accuracy of 99 % across the datasets. Improved compression along with high classification accuracy shows our proposed method's usefulness. © 2024 IEEE.

关键词： Image enhancement

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Factors Affecting Mental Health of High School Students due to COVID-19: An Exploratory Study

Factors Affecting Mental Health of High School Students due ...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Reza, Md. Rokonuzzaman Islam, Muhammad Nazrul Islam, Mohian Mumu, Afnan Alauddin Faisal, Faiz Al The International University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh LTIMindtree Ltd. Washington United States Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Mental health is one of the most significant factors in the human life span. It also influences how we interact with people, manage stress, and make good decisions. From childhood and youth through maturity, mental health is crucial at every stage of life. In the era of the COVID-19 pandemic, it becomes more vulnerable and affects teenagers as others. In this study, high school students' (10-19 years old) mental health states and the most contributing factors that affect their mental health were assessed through a statistical and machine learning-based approach. The study data were collected through an online survey and received responses from 158 respondents. The data were analyzed statistically with a chi-square test and path analysis considering all the possible factors of anxiety and depression to determine the most contributing factors. Besides, a few Machine Learning (ML) algorithms have been developed such as Logistic Regression(LR), Support Vector Machine(SVM), Naive Bayes(NB), AdaBoost(AB), and Random Forest(RF) for the prediction of anxiety and depression and found that AdaBoost achieved the highest score (86%). As outcomes, the overall analysis revealed that academic concerns such as poor understanding of online classes, daily activities like sleep pattern disruption, and extended uses of electronic devices are the major causes of the deterioration of high school students' mental health. © 2023 IEEE.

关键词： COVID-19

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Digital Image Encryption Based On Elliptic Curve Cryptography 4

Digital Image Encryption Based On Elliptic Curve Cryptograph...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Hasan, Mahmoud H. S. Ahmed, Medht H. S. H. Alasmarya Islamic University College of Information Technology Dept. Computer Science Zliten Libya Higher Institute of Engineering Technology Dept. Communication Engineer Zliten Libya

ISBN: (纸本)9798350372632

Information security is currently a major problem for data communication. Cryptography has emerged as a treatment and is essential to information security systems. Techniques for encryption can effectively prevent attacks on images. Elliptic curve cryptography (ECC) is an approach of public-key encryption, depends on the algebraic grouping of elliptic curves over finite fields. The present study introduces an intriguing elliptic curve cryptography-based image encryption approach and applied it to various images. The proposed technique was tested. We computed the number of pixels change rate (NPCR), unified average changing intensity (UACI), and peak signal to noise ratio (PSNR) to assess the effectiveness of the suggested technique. © 2024 IEEE.

关键词： Geometry

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