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The Conglomerate Compression Approach for Visual pattern recognition Networks in Edge AI Applications 6

The Conglomerate Compression Approach for Visual Pattern Rec...

引用

6th IEEE Pune Section international conference, PuneCon 2023

作者： Bhalgaonkar, Seema Munot, Mousami Anuse, Alwin SCTR's Pune Institute of Computer Technology & PES's Mcoe Sppu Dept of E&tc Pune India SCTR's Pune Institute of Computer Technology Sppu Dept of E&tc Pune India School of Ece Dr. Vishwanath Karad MIT-WPU Pune411038 India

ISBN: (纸本)9798350324204

Deep Neural Networks have shown remarkable performance in various applications of Visual pattern recognition (VPR). This field continues to grow due to emergence of new architectures, availability of huge data, and powerful computing resources. However, because of their high computational complexity that subsequently demands higher memory requirements, and power consumption, the deployment of such DNN-based Visual pattern recognition Networks (VPRN) models in edgeAI applications is hindered. Such overheads necessitate the compression of VPRN models while retaining their performance. This paper explores multiple compression techniques which combines Knowledge Distillation (KD), pruning and quantization that are applied to DNNs to achieve higher compression ratio. © 2023 IEEE.

关键词： Distillation

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Narratives in Teaching Artificial Intelligence Technologies

Narratives in Teaching Artificial Intelligence Technologies

引用

international conference on soft computing and Measurements (SCM)

作者： Petr N. Afonin I. P. Aleshin E. I. Antonova A. I. Krasnova Russian Customs Academy St. Petersburg Russia Technopark «Quantorium» (Municipal Autonomous institution of additional Education «Vladivostok City Palace of Creativity») Vladivostok Russia Vladivostok Branch of the Russian Customs Academy Vladivostok Russia St. Petersburg named after V. B. Bobkov branch of the Russian Customs Academy St. Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

The article considers the development of a narrative approach to teaching artificial intelligence in a departmental university based on the formation of simulator systems that allow implementing integrated approaches to pattern recognition on X-ray images obtained using inspection and inspection complexes. The decisive rule for evaluating learning outcomes in the context of the developed model is the possibility of comparing the results with the results of pattern recognition obtained using the author’s method of automatic object recognition based on a neural network approach.

关键词： Educational programs Neural networks Process control Learning (artificial intelligence) Inspection Big Data pattern recognition

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SMS Phishing Dataset for Machine Learning and pattern recognition 1

引用

14th international conference on soft computing and pattern recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2022

作者： Mishra, Sandhya Soni, Devpriya Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Sector-128 Noida201304 India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

The reliability of the dataset is an essential factor for solving classification problems. Data is required for training, testing, classification, and evaluation of the machine learning models. SMS Phishing (Smishing) is a binary classification problem in which messages are categorized as malicious (Smishing) or legitimate (Ham). It is a fraudulent activity in which the attacker sends a malicious text message to the Smartphone user that causes financial or personal loss to the victim. Few research works have been proposed for the identification of smishing messages. According to the literature survey conducted, the smishing dataset is not publicly available yet. Hence, we have composed a smishing dataset that contains smishing messages extracted from different internet sources. We have formulated a dataset of 5971 text messages that contain 638 smishing messages, 489 spam messages, and 4844 ham messages. This SMS Phishing dataset can be used for the extraction of smishing features and classification of text messages using Machine Learning Algorithms. Experimental evaluation of the dataset for smishing message categorization using keyword classification is also presented in this paper. This smishing dataset can be used as a baseline for future research work corresponding to SMS Phishing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile security

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pattern recognition and Modelling in Electrocardiogram Signals: Early Detection of Myocardial Ischemia and Infraction 2

Pattern Recognition and Modelling in Electrocardiogram Signa...

引用

2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Muthalagu, R. Ramachandran, R. Anuradha, T. Anupama, P.H. Jose Anand, A. Agni College of Technology Department of Electronics and Communication Engineering Tamil Nadu 600130 India Rajalakshmi Engineering College Department of Bio Medical Engineering Tamil Nadu 602105 India Kcg College of Technology Department of Electrical and Electronics Engineering Tamil Nadu 600097 India Department of Electronics and Communication Engineering KCG College of Technology Tamil Nadu 600097 India

ISBN: (纸本)9798350347579

pattern recognition and modeling in electrocardiogram (ECG) signals play an important role in the early detection of myocardial ischemia and infarction, which are serious cardiovascular diseases that require immediate medical attention. This study provides an overview of techniques used for pattern recognition and modeling in ECG signals to aid in the early detection of myocardial ischemia and infarction. First, the article discusses the importance of ECG signals in the diagnosis and monitoring of heart diseases. It highlights specific ECG changes associated with myocardial ischemia. Understanding these ECG patterns is critical for accurate diagnosis and timely intervention. Next, the study explores various techniques used for pattern recognition and modeling in ECG signals. These techniques include classical signal processing methods, filtering, feature extraction and classification algorithms, and advanced approaches such as machine learning and deep learning. The study discusses the strengths and limitations of each technique and their applications in the diagnosis of myocardial ischemia and myocardial infarction. Also, the study addresses challenges in analyzing ECG signals such as noise, artifact interference, and the need for real-time processing. It also discusses the importance of a comprehensive database of annotated ECG signals for training and testing pattern recognition models. Finally, the potential benefits of early detection of myocardial ischemia and myocardial infarction include improved patient outcomes, reduced morbidity and mortality, and efficient use of healthcare resources. This emphasizes the need for further research and development in pattern recognition and modeling techniques to improve the accuracy and efficiency of early detection methods. © 2023 IEEE.

关键词： Cardiology

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Research on pattern recognition of Urban and Rural Consumption Behavior Based on Machine Learning and Its Application in Digital Economy 2

Research on Pattern Recognition of Urban and Rural Consumpti...

引用

2nd international conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Mu, Xiaowei Liu, Yan Sheng, Zhijun Chen, Jiahui Changchun Normal University Changchun Humanities and Sciences College Changchun China Changchun Humanities and Sciences College Changchun China Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798350386639

This paper introduces an SVM algorithm for studying consumer behavior. This model is a new pattern recognition algorithm based on statistical principle. It has unique advantages in dealing with small samples, nonlinearity and high dimensional pattern recognition. The fuzzy semantic modeling method is constructed by using emotion computing and text mining. At the same time, the internal and external inducement factors are the first part and the purchasing degree of consumers is the second part, and the fuzzy reasoning model is established. The consumption behavior and process of urban and rural residents are constructed by using probability theory. Through the analysis of the relationship between the elements, the corresponding knowledge base is established. The unreasonable purchase degree model of customers is established by Jess method. Finally, taking a tobacco enterprise as an example, an empirical study was made on it. The results show that in urban and rural areas, SVM-based consumer behavior identification and statistical analysis has high accuracy and efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid soft computing Based Approach for Ageing in Face recognition 2

Hybrid Soft Computing Based Approach for Ageing in Face Reco...

引用

2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Sinha, Divyanshu Pandey, J.P. Chauhan, Bhavesh Uttarakhand Technical University Dehradun India Madan Mohan Malviya University of Technology Department of Electrical Engineering Gorakhpur India Srmcem Lucknow Department of Electronics and Communication Engineering India

ISBN: (纸本)9781665437899

soft computing is a set of techniques for addressing special class of complex problems which involve the use of computationally intelligent systems. The fusion of Deep Learning and soft computing tools like Neural Networks has shown a promising road ahead for computationally intensive problems, giving rise to algorithms and solutions with high efficiency and accuracy. Application of this fusion technique considerably improves the accuracy of recognizing faces in a facial recognition pipeline. Our hybrid model comprising of CNN & LSTM utilizes DCN'S which work on the concept of optimization of the embedding's of the face itself. Our proposed system has been tested on the CACD dataset and gives very promising & encouraging results © 2022 IEEE.

关键词： Face recognition

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Privacy-Preserving Average Consensus for Multi-Agent System with Event-Triggered Method 6

Privacy-Preserving Average Consensus for Multi-Agent System ...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Shi, Shuyu Cao, Xinyang Zhang, Qingmiao Chengdu Foreign Languages School International Department Chengdu China

ISBN: (纸本)9798350325485

The paper considers the privacy-preserving and communication constraints for multi-Agent systems. Firstly, a lightweight, decentralized, time-varying transformation method is introduced to prevent information leakage during information exchange between agents. This approach requires less computation and is compatible with systems with limited computing resources. Secondly, the paper proposes an event-Triggered mechanism that considers both privacy-preserving and communication constraints to avoid continuous updates of the privacy-preserving controller. By using intermittent communication mode, this approach effectively reduces computing and communication resources compared to traditional time-Triggered methods. Overall, these methods offer efficient solutions for privacy and communication challenges in multi-Agent systems. The finite-Time privacy-preserving control protocol is validated through numerical simulation examples to demonstrate its effectiveness. © 2023 IEEE.

关键词： Multi agent systems

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Boundary Defense Against Black-box Adversarial Attacks 26

Boundary Defense Against Black-box Adversarial Attacks

引用

26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Aithal, Manjushree B. Li, Xiaohua SUNY Binghamton Dept Elect & Comp Engn Binghamton NY 13902 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Black-box adversarial attacks generate adversarial samples via iterative optimizations using repeated queries. Defending deep neural networks against such attacks has been challenging. In this paper, we propose an efficient Boundary Defense (BD) method which mitigates black-box attacks by exploiting the fact that the adversarial optimizations often need samples on the classification boundary. Our method detects the boundary samples as those with low classification confidence and adds white Gaussian noise to their logits. The method's impact on the deep network's classification accuracy is analyzed theoretically. Extensive experiments are conducted and the results show that the BD method can reliably defend against both soft and hard label black-box attacks. It outperforms a list of existing defense methods. For IMAGENET models, by adding zero-mean white Gaussian noise with standard deviation 0.1 to the logits of those images with classification confidence less than 0.3, the defense reduces the attack success rate to almost 0 while limiting the classification accuracy degradation to around 1 percent.

关键词： Degradation Limiting Gaussian noise Neural networks Closed box Reliability theory pattern recognition

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Evaluating Communication pattern Representations in Execution Trace Gantt Charts

Evaluating Communication Pattern Representations in Executio...

引用

2024 IEEE Working conference on software Visualization

作者： Scully-Allison, Connor Isaacs, Katherine E. Univ Utah Sci Comp & Imaging Inst SCI Salt Lake City UT 84112 USA

ISBN: (纸本)9798331528492;9798331528485

Gantt charts are frequently used to explore execution traces of large-scale parallel programs. In these visualizations, each parallel processor is assigned a row showing the computation state of a processor at a particular time. Lines are drawn between rows to show communication between these processors. When drawn to align equivalent calls across rows, visual patterns can emerge reflecting communication behavior of the executing code. However, though these patterns have the same definition at any scale, they can be obscured by the density of rendered lines when displaying more than a few hundred processors. We seek to understand the effectiveness of various strategies for recognizing these patterns in Gantt charts. Specifically, we conduct a pre-registered user study comparing recognition of patterns when viewing all processors, a subset of processors, or a set of abstracted glyphs overlaid on the chart. We find that all strategies have limitations when scaling, motivating further designs. Our results further indicate that for simple patterns, the glyphs are more effective in general pattern recognition while the zoomed subsets provide nuance to specific characteristics, such as offsets, in patterns. These results suggest the development of a combined approach may be appropriate to enable pattern comprehension in large-scale Gantt charts.

关键词： visualization high performance computing parallel programming communication gantt chart

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Multilayer Online Self-Acquired Knowledge Distillation 26

Multilayer Online Self-Acquired Knowledge Distillation

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Tzelepi, Maria Symeonidis, Charalampos Nikolaidis, Nikos Tefas, Anastasios Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Online knowledge distillation has been proposed as an auspicious approach for circumventing the flaws of the conventional offline distillation (i.e., complex, and computationally and memory demanding process). In this work, a novel online self-distillation method, named Multilayer Online Self-Acquired Knowledge Distillation (MOSAKD), is proposed, aiming to develop fast-to-execute and effective models that can comply with applications with memory and computational restrictions, e.g., robotics applications. The MOSAKD method is able to mine additional knowledge both from the intermediate and the output layers of a deep neural model in an online fashion. To achieve this goal, k-nn non-parametric density estimation for estimating the unknown probability distributions of the data samples in the feature space generated by any neural layer is used. This enables us to compute the soft labels that explicitly express the similarities of the data with the classes, by directly estimating the posterior class probabilities of the data samples. The experimental evaluation on four datasets, including a dataset of synthetic images, indicates the effectiveness of the MOSAKD method and the superiority over existing online distillation methods.

关键词： Training Computational modeling Pipelines Estimation Nonhomogeneous media Probability distribution pattern recognition

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