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共 729 条记录，以下是91-100 订阅

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On the Use of VGs for Feature Selection in Supervised machine learning - A Use Case to Detect Distributed DoS Attacks 3rd

On the Use of VGs for Feature Selection in Supervised Machin...

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3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Lopes, Joao Partida, Alberto Pinto, Pedro Pinto, Antonio Inst Politecn Viana Do Castelo ADiT Lab Viana Do Castelo Portugal Rey Juan Carlos Univ Data Complex Networks & Cybersecur Sci Technol In Madrid Spain Univ Maia Maia Portugal Inst Politecn Porto ESTG CIICESI Felgueiras Portugal INESC TEC Porto Portugal

ISBN: (纸本)9783031530241;9783031530258

Information systems depend on security mechanisms to detect and respond to cyber-attacks. One of the most frequent attacks is the Distributed Denial of Service (DDoS): it impairs the performance of systems and, in the worst case, leads to prolonged periods of downtime that prevent business processes from running normally. To detect this attack, several supervised machine learning (ML) algorithms have been developed and companies use them to protect their servers. A key stage in these algorithms is feature pre-processing, in which, input data features are assessed and selected to obtain the best results in the subsequent stages that are required to implement supervised ML algorithms. In this article, an innovative approach for feature selection is proposed: the use of Visibility Graphs (VGs) to select features for supervised machine learning algorithms used to detect distributed DoS attacks. The results show that VG can be quickly implemented and can compete with other methods to select ML features, as they require low computational resources and they offer satisfactory results, at least in our example based on the early detection of distributed DoS. The size of the processed data appears as the main implementation constraint for this novel feature selection method.

关键词： Artificial intelligence machine learning Supervised learning Denial of Service attack Visibility Graph Cybersecurity

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Research on Transformer Fault Voice Print Information processing Based on Wavelet Packet Optimization Transform 3

Research on Transformer Fault Voice Print Information Proces...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Wang, Lili Li, Zibin Li, Jianwu Lin, Wande Ren, Jiyun Yu, Wanhong Zhao, Bingnan Chen, Nan Zhao, Dianzhe Shenyang Institute of Engineering Shenyang110136 China

ISBN: (纸本)9781510664814

In recent years, more and more attention has been paid to the study of transformer voiceprint. Therefore, this paper mainly studies the denoising technology of transformer voiceprint signal in high noise environment, compares the wavelet packet transform and the wavelet transform, respectively, and finds that the de-noising effect of wavelet packet transform is better than that of wavelet transform. The three-layer decomposition of vowels in the high-frequency part is conducive to the subsequent extraction and identification of fault signals. © 2023 SPIE.

关键词： Wavelet analysis

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Enhancing the Long-Term Sustainability of Healthcare Data Optimization Through Deep Federated learning 3

Enhancing the Long-Term Sustainability of Healthcare Data Op...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Kumar, Balraj Evin, Munni Byeon, Haewon Sivaprakash, M. Sheeba, P. Maria Sunil, J. School of Computer Application Lovely Professional University Punjab Phagwara India School of Engineering and Computing American International University Kuwait College of Ai Convergence Inje University Department of AI-Big Data Gimhae50834 Korea Republic of Stella Mary's College of Engineering Department of Mechanical Engineering Tamil Nadu 629202 India St. Mother Theresa Engineering College Department of Electronics and Communication Engineering Thoothukudi India Annai Vailankanni College of Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798350361186

The medical industry regards the prevention and detection of skin diseases as a top priority. The number of people suffering from skin diseases is increasing faster than ever before due to increased levels of pollution and poor diet. Although it's not the major priority, having bad skin makes us less certain. Regularly inspecting your skin is an important step in identifying any degenerative or early changes in your skin that could lead to skin disease. machine learning algorithms can be utilized to create robust models that can categorize a wide range of skin conditions. First, the skin and non-skin must be distinguished in order to identify skin conditions. In order to determine the unambiguous a group of skin conditions A selection of five distinct machine learning algorithms has been employed to compile a dataset of skin infections. These algorithms include Kernel Self- pattern, Random Forest, Naive Bayes, Logistic Regression, and CNN, among others. A comparable investigation based on training accuracy and skepticism matrix criterion has been carried out and illustrated with graphs. It has been determined that, of all the applicants, CNN offers the highest level of training accuracy for the appropriate assumption of skin disorders. © 2024 IEEE.

关键词： Deep learning

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Screening of Citrus Diseases Using Deep learning Embedders and machine learning Techniques 3

Screening of Citrus Diseases Using Deep Learning Embedders a...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Challagundla, Yagnesh Chintalapati, Lohitha Rani Tunuguntla, Trilok Sai Charan Sur, Amit Roy, Bappadittya Zhuo, Eugenia R. VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati India HIT Department of IT West Bengal India VIT-AP University School of Electronics Andhra Pradesh Amaravati India University of Santo Tomas Manila College of Information and Computing Sciences Philippines

ISBN: (纸本)9798350320749

A tropical fruit with great economic significance, citrus is cultivated and farmed in almost 150 different nations. A total of about 157.98 million tons of citrus fruit are produced worldwide. Citrus plant diseases have a major negative impact on the citrus fruit market as well as production in terms of quantity as well as quality. Attention must be paid to citrus fruits and leaves in order to promptly diagnose and treat problems;failing to do so results in severe financial loss. Citrus plant diseases cause a yearly loss of about 50% of citrus fruits at citrus farming operations. In this study, to produce the vector representation of the images, we used three deep learning image embedders namely Inception V3, VGG16, and Squeeze net. We used five machine learning models, including Random Forest, KNN, Gradient Boosting, SGD, and Neural Network with 5-fold Cross Validation, to predict citrus diseases. Using the considered dataset, the classification performance of each independently tuned ML model across all of its ensembles has been obtained. The neural network with Inception V3 as image embedder has obtained maximum average accuracy of 96.6%, 96.6% recall, 96.5% precision, and 96.5% F1 Score. Finally, our prediction model offers a reliable technique for accurate detection of citrus diseases to assist farmers. © 2023 IEEE.

关键词： Citrus fruits

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A Literature Survey on Classification of Electrocardiogram(ECG) Abnormalities 3

A Literature Survey on Classification of Electrocardiogram(E...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Betha, Sashi Kanth Sreya Sri, K. Jyotshna, L. Raji Naga Sai, L. Nikhita, P. Vignan's Institute of Engineering for Women Department of E.C.E Duvvada India Duvvada India

ISBN: (纸本)9798350343632

The ECG signal is used in the field of cardiology to monitor and analyze the activity of the heart. This focuses on development and implementation of advanced machine learning algorithms and signal processing techniques for the automated classification and identification of abnormalities in ECG signals. The literature survey is the study of accuracy and efficiency of ECG analysis, thereby aiding healthcare professionals in the timely diagnosis and treatment of cardiovascular diseases. To achieve this, a comprehensive dataset of ECG recordings is collected and preprocessed to extract relevant data from the ECG signals. Feature extraction techniques such as wavelet-based features, frequency domain, and temporal domain are used. A variety of methods, including neural networks (NN), LDA, SVM, KNN, decision trees, and Bayesian clarifiers, have been employed to organize ECG signals. In order to diagnose arrhythmias, a two-dimensional CNN model is used for ECG signal classification, with the help of CNN's LSTM. Deep learning has recently gained a lot of popularity and been effectively used for the classification of heart disease and arrhythmia, ST segment or classification of heartbeat. A variety of machine learning models, such as RNNs, HRNNs and ensemble techniques are trained and evaluated for their performance in classifying ECG signals into different categories, including normal rhythms and various cardiac arrhythmias. Additionally, anomaly detection algorithms are employed to identify subtle deviations from normal ECG patterns. The literature survey results studied from this research have the potential to enhance patient care by enabling early detection of cardiac abnormalities, leading to more timely interventions and improved clinical outcomes. Furthermore, the methodology explained in this survey can be applied to other areas of medical signal analysis, paving the way for more efficient and reliable diagnostic tools in the field of healthcare. The primary goals of

关键词： Arrhythmia Convolutional neural networks (CNNs) Electrocardiogram Handcrafted-Rule-enhanced Neural Network (HRNN) machine learning algorithms Recurrent neural networks (RNNs) Support Vector machine (SVM)

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Pre Cardiac Failure Detection using different supervised machine learning Methods with CNN 3

Pre Cardiac Failure Detection using different supervised Mac...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Gupta, Amit Shaikh, Meeravali Reddy, G. Jithender Chouhan, Arun Singh Malla Reddy University School of Engineering Department of Cyber Security Telangana India Malla Reddy University School of Engineering Department of CSE Telangana India GITAM University Visakhapatnam India

ISBN: (纸本)9798350320749

For aspect, a convolution Neural Network (CNN) for cardiac arrest would help establish a better output. Whilst a cardiac arrest occurs, the limited interventions available to shop-affected person's lives are related to badly affected outcomes. Therefore, the best way to enhance affected person results and lower related healthcare prices might be to prevent cardiac arrest. This remark highlights the significance of prediction models that continuously perceive excessive-risk individuals and assist healthcare providers in providing centered care to the appropriate patient at the appropriate time for the early identification of cardiac arrest patients who may need immediate medical attention. Moreover, the significant aspect lies in the proper symmetry formation or can, say, the better scenario to compute or make a clear recognition of the image having a clear resolution of the image. Starting with a small model to determine things as simple as CNN (Convolution Neural Network). In this proposed paper, we are used in seven different supervised machine learning method for simulation of ECG data set results in different time interval with different duration and amplitude. In the CNN with a partially connected convolution layer, the image has a single neuron connected to a small layer region. The CNN (convolution Neural Network) image classification takes the input, processes it and classification is done on the dimensions organized in three dimensions width, height, and depth. © 2023 IEEE.

关键词： Convolution

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machine learning based human recognition via robust Features from audio signals 3

Machine Learning based human recognition via robust Features...

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3rd IEEE international conference on Artificial Intelligence, ICAI 2023

作者： Sadique, Umar Khan, Muhammad Suleman Anwar, Shahzad Ahmad, Mehran University of Engineering and Technology AI in Healthcare Lab National Centre of AI Peshawar Pakistan University of Engineering and Technology Department of Electrical Engineering Bannu Campus Peshawar Pakistan University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan

ISBN: (纸本)9798350322125

Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their voice characteristic. This popular and beneficial biometric technique could be employed for identity human, security purposes, and many different applications. Human audio signal recognition consists of two phases i.e., Features Extraction and Classification. The proposed work consists of extracting features through the Mel Frequency Cepstral-Coefficient (MFCC) from the human audio signal, selecting robust features through Principal Component Analysis PCA, and classifying the selected features by comparing seven machine learning and proposed deep learning algorithms. Finally, compare the performance of different algorithms with different percentages of selected features to evaluate the acceptance rate of the correlated features. Support Vector machine SVM shows the best performance with an Accuracy of 99.27% with F1-score and ROC values of 1.00. In the comparison with other methods, the Random Forest and CNN-ANN are 2nd top robust models with an accuracy of 98.7%. Some of the algorithm's accuracy decreased with fewer features, including Naïve Bayes accuracy suddenly decreases to 60% on 20% of total features. The experiment concludes the acceptance rate of correlated features in different ML and DL algorithms are different in speech processing data. © 2023 IEEE.

关键词： learning algorithms

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Diabetes Prediction Using Nature-Inspired Optimization Algorithms 3

Diabetes Prediction Using Nature-Inspired Optimization Algor...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Vijaya, J. Tirkey, Divyanshu Singh, Priyank Goel, Aditya International Institute of Information Technology Department of DSAI Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350320749

Diabetes is a common genetic disease across the world. It is a chronic disease spurred by either production of insufficient production of insulin by the pancreas or less insulin use by the body. Insulin maintains the blood sugar level of the body. Uncontrolled diabetes can cause hypertension, also known as high blood glucose. Hypertension in the long run can be a cause of heart disease. Therefore, the prediction of diabetes is a major problem in the health sector. Various machine learning methods are already in use for the prediction of diabetes. Traditional methods were first used to address diabetes forecasting, but they take a while to deploy. Therefore, future research has adopted the use of various optimization strategies to solve the issue of diabetes forecasting as well as to boost system selectivity and operating speed. These methods have all proven to be successful in solving different optimization issues and also improve the model significantly. This work proposes a Logistic Regression model by the use of nature-inspired algorithms. We proposed a methodology which uses seven different optimization algorithms instead of gradient descent for linear regression. We implemented a linear regression algorithm from scratch and then used it with different optimization algorithms which improve the overall performance. © 2023 IEEE.

关键词： Blood

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Hybrid Deep learning Cloud Intrusion Detection 3

Hybrid Deep Learning Cloud Intrusion Detection

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Karnan, Lokanayaki Mahalakshmi, S.B. Thirunavukkarasu, V. Christ University Department of Computer Science Karnataka Bangalore560029 India Coimbatore Institute of Technology Department of Artificial Intelligence & Machine Learning Tamil Nadu Coimbatore641014 India

ISBN: (纸本)9798350361186

The scalability and flexibility that cloud computing provides, organisations can readily adapt their resources to meet demand without having to make significant upfront expenditures in hardware infrastructure. Three main types of computing services are provided to people worldwide via the Internet. Increased performance and resource access are two benefits that come with using cloud computing, but there is also an increased chance of attack. As a result of this research, intrusion detection systems that can process massive amounts of data packets, analyse them, and produce reports using knowledge and behaviour analysis were created. Convolution Neural Network Algorithm encrypts data as it's being transmitted end-to-end and is stored in the cloud, providing an extra degree of security. Data protection in the cloud is improved by intrusion detection. This study uses a model to show how data is encrypted and decrypted, of an algorithm and describes the defences against attacks. When assessing the performance of the suggested system, it's critical to consider the time and memory needed to encrypt and decrypt big text files. Additionally, the security of the cloud has been investigated and contrasted with various encoding techniques now in use. © 2024 IEEE.

关键词： Intrusion detection

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Graph neural networks for protein-protein interactions 3

Graph neural networks for protein-protein interactions

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3rd international conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Chang, Sheng Wang, Yifan Zhang, Xinhong Zhang, Fan School of Computer and Information Engineering Henan University Kaifeng475004 China School of Software Henan University Kaifeng475004 China

ISBN: (纸本)9781510668522

Protein-protein interaction (PPI) is the process by which two or more protein molecules form a protein complex through non-covalent bonds. The interactions between protein and the protein complexes formed by these interactions are the primary complements of various essential cellular functions and carry out almost all vital life activities. Most PPI research today focuses on the use of machine learning methods, of which the use of graph neural networks (GNN) for prediction is a current hot direction. This paper highlights the main advances in the application of GNN to proteinprotein interactions. The first part reviews protein sequence-based methods for PPI prediction. And the second part focuses on structure-based graph neural network PPI prediction methods. Finally, we discuss the shortcomings of GNNs in this area and future directions in PPI prediction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

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