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Automation Methods of Data Collection and Analysis for Managing Complex IT Infrastructures

Automation Methods of Data Collection and Analysis for Manag...

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international Russian Smart Industry conference (SmartIndustryCon)

作者： Anton Skalkin Anton Romanov Department of Information Systems Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article discusses an approach to the organization of monitoring and management of server equipment, aimed at ensuring the stable operation of the IT infrastructure. Modern methods based on threshold analysis cannot cope with the volume and variety of data, which requires automation of the collection, processing and analysis of metrics. A computing cluster of servers using the Proxmox VE virtualization platform is presented as an object of research. The features of time series storage and data processing using the InfluxDB database, which has a convenient query language called Flux, are described. Implemented software architecture for integrating monitoring and data analysis tools, including Prometheus PVE Exporter, Telegraf and Flux. The advantages of using Python for metric analysis and query development using the Builder pattern are shown, which simplifies working with the system. Methods of data preprocessing and visualization for anomaly analysis and forecasting with the aim of maintaining the stable operation of server equipment are discussed. The proposed approach ensures the efficiency and ease of monitoring, while also enhancing the accuracy of decision-making given the increasing complexity of IT infrastructure.

关键词： Measurement Automation software architecture Source coding soft sensors Time series analysis Virtual machines Servers Monitoring Python

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

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Visual Analytics and Data Visualization (ICVADV), international conference on

作者： Venkateswaran Radhakrishnan Vijayakumari Rodda S. Sowmiya Abdur Rahman Sarker R. Palanikumar R. Asokan Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Dhofar Sultanate of Oman Department of Computer Science Krishna University Rudravaram Machilipatnam Sri Eshwar College of Engineering COIMBATORE Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of ECE Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security.

关键词： Deep learning Ciphers Accuracy Computational modeling Visual analytics Neural networks Transformers Encryption pattern recognition Convolutional neural networks

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SmartRecovery: A Deep Learning-Based System for Personalized Post-Cold Recovery Management in Diabetic Patients

SmartRecovery: A Deep Learning-Based System for Personalized...

引用

Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： G. Revathy J. Justina Princy Thilagavathy C Saranya M. Thangavel Department of CSE Srinivasa Ramunjan Centre Sastra Deemed University Kumbakonam Tamil Nadu Department of Computer App ications School of Computing VET Institute of Arts and Science (Co-education) College Thindal. Erode Tamil Nadu Department of Biomedical Engineering Nandha Engineering College Erode Tamil Nadu Department of MCA Erode Sengunthar Engineering College PerunduraiErode Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

People with diabetes frequently have a longer and more difficult time recovering from colds because of their weakened immune systems and altered metabolism. An method that is more individualized is required since traditional recovery regimens do not take into consideration lifestyle variables, pharmaceutical interactions, and individual differences in glucose levels. A deep learning-based solution called SmartRecovery is suggested in this study to help diabetics control their post-cold recovery as best they can. The system creates adaptive recovery plans by utilizing multimodal data, such as patient vitals, glucose monitoring, medication adherence, and symptom progression. Using a combination of recurrent neural networks (RNNs) for sequential health data analysis and convolutional neural networks (CNNs) for pattern recognition, SmartRecovery predicts potential complications and recommends personalized interventions. In order to ensure ongoing evaluation and feedback, the system incorporates real-time monitoring via wearable sensors and mobile health applications. Experimental assessments using actual diabetes patient datasets show how well the suggested strategy works to speed up recovery and reduce dangers to health. A revolutionary approach to post-illness care, SmartRecovery improves patient outcomes and lessens the strain on healthcare systems by utilizing AI-driven insights.

关键词： Deep learning Telemedicine Real-time systems Diabetes pattern recognition Glucose Monitoring Wearable sensors Standards Strain

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Advanced Novel NLP Approach for Emotion Detection in Poetry

Advanced Novel NLP Approach for Emotion Detection in Poetry

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Advances in computing, Communication and Applied Informatics (ACCAI), international conference on

作者： J. Dafni Rose S Bhuwaneshwaran S Dhiyanesh Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Old Mamallapuram Road Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

In the era of big data, characterized by the exponential growth of textual information across diverse domains, there arises a pressing need for sophisticated techniques in sentiment analysis. Serving as a vital component of natural language processing, sentiment analysis involves the extraction and comprehension of subjective information from textual data. This paper introduces an innovative and comprehensive framework for sentiment analysis, strategically designed to yield profound insights and provide decision support in the realm of textual data. The proposed framework harnesses the power of state-of-the-art machine learning and deep learning techniques, aiming to elevate both the accuracy and granularity of sentiment analysis. Adopting a multi-faceted approach, it seamlessly integrates traditional lexical-based methods with cutting-edge neural network architectures, enabling the model to adeptly capture subtle nuances and context-dependent sentiments. Additionally, the framework incorporates domain-specific lexicons and embeddings, tailoring sentiment analysis to the intricacies of diverse industries. To ensure practical applicability and scalability, the framework is implemented and rigorously evaluated across a spectrum of datasets encompassing social media, customer reviews, and industry-specific corpora. The results underscore the robustness of the framework in effectively handling disparate data sources and its remarkable adaptability to nuanced expressions of sentiment.

关键词： Sentiment analysis Emotion recognition Social networking (online) Reviews soft sensors Scalability Neural networks

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Enhancement of traditional clothes pattern recognation using Convolutional Neural Network

Enhancement of traditional clothes pattern recognation using...

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IEEE international conference on computing (ICOCO)

作者： Rangkuti, Abdul Haris Athala, Varyl Hasbi Luthfi, Naufal Fauzi Aditama, Syaugi Vikri Ramadhan, Muhammad Maulana Aslamia, Ayu Hidayah Bina Nusantara Univ Sch Comp Sci Bandung Indonesia

ISBN: (纸本)9781665436892

In general, Traditional clothes in Indonesia has its own characteristics to recognize the origin of the clothes. The formation of traditional clothes patterns in Indonesia from the crafts of remote communities in the regions of Indonesia. The article is that Indonesian citizens still find it difficult to distinguish traditional clothes that are scattered throughout Indonesia. To support this research, we use the Convolutional neural network method for types VGG16, VGG19 and MobileNetV2. To support the recognition process for a variety of traditional clothes patterns, the pattern processing process has started from preprocessing and image segmentation. This research was conducted on 5 types of experiments with supporting parameters, namely the number of training images and test images, rotation and scale of each image, image class and CNN parameters. After we conducted experiments on a collection of images that could be found in various scales and degrees, the average classification accuracy using VGG 16 was 79,23% in condition 2, but the highest classification accuracy was between 84,2%-92,5%. Meanwhile, if using VGG 19 the average classification accuracy is 79,95% with condition 1 for the highest accuracy between 92,6%-96,3%. If using mobilenetV2 the average classification accuracy is 83,44% for the highest accuracy between 87,3%-96,4%. Basically Research for traditional clothes pattern recognition using Deep CNN has succeeded in recognizing some traditional clothes patterns up to more than 92%.

关键词： Traditional clothes Convolutional neural network VGG 16 VGG 19 MobilenetV2 classification accuracy

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Weakly Supervised Medical Image Segmentation with soft Labels and Noise Robust Loss 26th

Weakly Supervised Medical Image Segmentation with Soft Labe...

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26th international conference on pattern recognition, ICPR 2022

作者： Felfeliyan, Banafshe Hareendranathan, Abhilash Kuntze, Gregor Wichuk, Stephanie Forkert, Nils D. Jaremko, Jacob L. Ronsky, Janet L. Department of Biomedical Engineering University of Calgary CalgaryAB Canada McCaig Institute for Bone and Joint Health University of Calgary CalgaryAB Canada Department of Radiology and Diagnostic Imaging University of Alberta EdmontonAB Canada University of Alberta EdmontonAB Canada Mechanical and Manufacturing Engineering University of Calgary CalgaryAB Canada

ISBN: (纸本)9783031377419

Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-labeled annotation is not only expensive but also is subjective, error-prone, and inter-/intra- observer variability introduces noise to labels. This is particularly a problem when using deep learning models for segmenting medical images due to the ambiguous anatomical boundaries. Image-based medical diagnosis tools using deep learning models trained with incorrect segmentation labels can lead to false diagnoses and treatment suggestions. Multi-rater annotations might be better suited to train deep learning models with small training sets compared to single-rater annotations. The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a "noise-tolerant loss" function. The model was evaluated by comparing it to binary ground truth for 17 knees MRI scans for clinical segmentation and detection of bone marrow lesions (BML). The proposed method successfully improved precision 14, recall 22, and Dice score 8 percent compared to a binary cross-entropy loss function. Overall, the results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels. © 2023, Springer Nature Switzerland AG.

关键词： Diagnosis

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A Facial Expression recognition System for Smart Learning Based on YOLO and Vision Transformer 2021

A Facial Expression Recognition System for Smart Learning Ba...

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7th international conference on computing and Artificial Intelligence, ICCAI 2021

作者： Ling, Xufeng Liang, Jingxin Wang, Dong Yang, Jie Shanghai Normal University Tianhua College Ai School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781450389501

This paper proposes a facial expression recognition system for smart learning on classroom. Firstly, YOLO is used to extract face images of multiple students from high-resolution video;secondly, face images are preprocessed, then a self-attention based model named Vision Transformer (ViT) is used to recognize facial expressions;finally, the classified facial expression is used to assist teacher to analyze students' learning status, so as to provide suggestions for improving teaching effect. © 2021 ACM.

关键词： Students

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Applying Reservoir computing and Machine Learning Techniques for Image Enhancement in Biomedical Imaging

Applying Reservoir Computing and Machine Learning Techniques...

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international conference on Smart Communications and Networking (SmartNets)

作者： Andre Slonopas Adam Beatty Yenni Djajalaksana Department of Cybersecurity American Public University Sysytem (APUS) School of Security and Global Studies (SSGS) Charles Town WV International Council of E-Commerce Consultants (EC-Council) Academia Division Tampa FL

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

The paper presents a comprehensive study on the application of Machine Learning (ML) in enhancing the contrast of biomedical images. This research is pivotal in addressing the challenges of low visibility and detail in medical imaging, which are crucial for accurate diagnosis and treatment. The paper introduces innovative ML-based Histogram Equalization (ML - HE), leveraging deep learning algorithms and advanced image processing methods, to significantly improve the quality of biomedical images. These techniques enhance image clarity, detail, and overall contrast without compromising the integrity of the original data. This paper seeks to explore the integration of ML, specifically Reservoir computing, with traditional image enhancement methods, creating a synergistic approach that leverages the strengths of both ML and conventional techniques and expedites image enhancements in near real-time. This hybrid approach is shown to be more effective in handling diverse and complex imaging scenarios encountered in biomedical applications. The study also discusses the implications of these advancements for medical professionals, highlighting how ML-enhanced images can lead to more accurate diagnoses, better patient outcomes, and advancements in medical research. Overall, this paper sheds light on the transformative potential of ML in revolutionizing biomedical imaging, setting a new standard for image quality and diagnostic precision in healthcare.

关键词： Histograms Visualization Accuracy Machine learning algorithms soft sensors Thermocouples Speech recognition

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Implementing a Robust IoT Solution to Monitor Prone Position using Machine Learning Techniques 11

Implementing a Robust IoT Solution to Monitor Prone Position...

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11th IEEE international conference on Communication Systems and Network Technologies, CSNT 2022

作者： Belay, Semagn Shifere Prince, T. Beyene, Tizazu Bayih Marew, Walelegne Yirdew Kebede, Andebet Dessiewu Woldia University School of Computing Woldia Institute of Technology Ethiopia College of Science and Arts University Department of Computer Science United Arab Emirates

ISBN: (纸本)9781665480383

It is essential to monitor sleeping patterns and position in order to learn about one's health state in order to enhance it and minimize sleep apnea. The use of a bed-mounted seismometer device to track the heart and respiratory patterns, as well as physical movement and attitude, while sleeping is discussed in this paper. The Internet of Things (IoT) and its gadgets and systems are becoming a part of everyday life. They are primarily used to track everyday tasks, particularly those that are relevant to individual health and wellness. As a matter of fact, and it's becoming increasingly important throughout the COVID-19 outbreak. A intelligent tracking and warning Internet of Things system known "NEF"was updated in this work to detect on-bed movement patterns, such as the prone position, using a variety of neural network models. On-bed movement patterns were recorded from seven different participants. The models derived using deep network were the most accurate when just the prone and supine postures were included. Random forest, on the other hand, produced the greatest total proportion of properly analysis refers to a set. Beddings such as pillows and blankets are expected to be investigated in greater depth in the future. © 2022 IEEE.

关键词： pattern recognition

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One-shot Distributed Algorithm for Generalized Eigenvalue Problem 22

One-shot Distributed Algorithm for Generalized Eigenvalue Pr...

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Lv, Kexin Sun, Zheng He, Fan Huang, Xiaolin Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Quality and Business Operation Department Saic Motor Corporation Limited 489 Wei Hai Road Shanghai200041 China

ISBN: (纸本)9781450396110

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. The generalized eigenvalue problem (GEP) plays a vital role in a large family of high-dimensional statistical models. However, the existing distributed method for eigenvalue decomposition cannot be applied in GEP for the divergence of the empirical covariance matrix. Here we propose a general distributed GEP framework with one-shot communication for GEP. If the symmetric data covariance has repeated eigenvalues, e.g., in canonical component analysis, we further modify the method for better convergence. The theoretical analysis on approximation error is conducted and the relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers is analyzed. Numerical experiments also show the effectiveness of the proposed algorithms both in the synthetic dataset and real-world datasets. © 2022 ACM.

关键词： Covariance matrix

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