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Progressive Clustering: An Unsupervised Approach Towards Continual Knowledge Acquisition of Incremental data 3rd

Progressive Clustering: An Unsupervised Approach Towards Con...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Gunari, Akshaykumar Kudari, Shashidhar V. Tabib, Ramesh Ashok Mudenagudi, Uma KLE Technological University Hubli India

ISBN: (纸本)9783031092817

In this paper, we propose a categorization strategy to handle the incremental nature of data by identifying concepts of drift in the data stream. In the world of digitalization, the total amount of data created, captured, copied, and consumed is increasing rapidly, reaching a few zettabytes. Various fields of data mining and machine learning applications involve clustering as their principal component, considering the non-incremental nature of the data. However, many real-world machine learning algorithms need to adapt to this ever-growing global data sphere to continually learn new patterns. In addition, the model needs to be acquainted with the continuous change in the distribution of the input data. Towards this, we propose a clustering algorithm termed as Progressive Clustering to foresee the phenomenon of increase in data and sustain it until the pattern of the data changes considerably. We demonstrate the results of our clustering algorithm by simulating various instances of the incremental nature of the data in the form of a data stream. We demonstrate the results of our proposed methodology on benchmark MNIST and Fashion-MNIST datasets and evaluate our strategy using appropriate quantitative metrics. © 2022, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Developments in Text Extraction from Images: Comparative Evaluation of Various Techniques

Developments in Text Extraction from Images: Comparative Eva...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Akash Burnwal Nishi Choudhary Saurav Kumar Mamata P. Wagh Hrishika Computer Science And Engineering C.V. Raman Global University Bhubaneswar India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Extracting text from images is essential in image processing and computer vision, with applications in document digitization and automated text recognition. this paper reviews various text extraction techniques, categorized into thresholding, rough set and fuzzy set methods, clustering, edge detection, and machine learning. thresholding techniques such as Gaussian, Otsu, adaptive, and double-edge methods are explored. Rough set and fuzzy set methods, which handle uncertainty in image data and improve text segmentation, are reviewed. Clustering techniques, such as K-means and density-based methods, are studied for their effectiveness in grouping pixels for text isolation. Edge detection techniques, including Sobel, Roberts, Canny, Morphological Component Analysis (MCA), and Laplacian, are examined for their role in enhancing text boundary identification. machine learning approaches, such as Support Vector machines (SVM), Contrastive Language-Image Pre-training (CLIP), Hidden Markov Models (HMM), and hybrid BiLSTM-CNN models, are analyzed for their ability to improve accuracy in noisy environments. this review compares these techniques, highlighting their strengths, challenges, and applications for text extraction.

关键词： Support vector machines Fuzzy sets thresholding (Imaging) Accuracy Text recognition Reviews Image edge detection Hidden Markov models Rough sets machine learning

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A Guiding learning Model for Transformer Fault Diagnosis Combining Rule-Based Domain Knowledge and Extreme learning machine 4

A Guiding Learning Model for Transformer Fault Diagnosis Com...

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4th IEEE China international Electrical and Energy conference, CIEEC 2021

作者： Zhang, Zhongyi Liang, Yongliang Ma, Zhao Shandong University School of Electrical Engineering Jinan China

ISBN: (纸本)9781728171494

machine learning algorithms based on data mining have been continuously developed for half a century and have achieved good application effects in many fields including transformer fault diagnosis. However, mining rules only by data and using empirical risks to fit structural risks will have a certain negative impact on the generalization performance of the model and the security of the output results. When it is applied to transformer fault diagnosis, it is easy to diagnose serious fault as non-serious fault. In this paper, we introduce the characteristics of the domain experiential knowledge based approach and the machine learning model based approach, then a concept called guiding learning is introduced. On the basis of using Receiver operating characteristics curve (ROC) to verify and mine the relevant experience knowledge in transformer fault diagnosis field, combined with the extreme learning machine (ELM) model, a guiding learning model for transformer fault diagnosis is established. After that, the particle swarm optimization (PSO) algorithm is used to optimize the model, and in this process, the trust degree that changes with the number of iterations is introduced to better adapt to the training process of the model. By comparing with the ELM fault diagnosis model optimized by particle swarm optimization, it is proved that the established guiding learning model is superior to the traditional model in generalization performance and safety of output results. At the same time, the probability of serious diagnostic errors is dramatically reduced. © 2021 IEEE.

关键词： Domain Knowledge

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A Residual Neural Network-Based Incremental learning Model for Driver State Perception

A Residual Neural Network-Based Incremental Learning Model f...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Jie Cai Jingming Zhang Erxin Sun Jianjun Cai Research and Development Department Automotive Data of China (Tianjin) Company Ltd Tianjin China

this paper presents a model for implementing active machine sensing of fatigue states in an intelligent cockpit. To make the model more robust and achieve higher accuracy, the model introduces residual modules in the convolutional neural network and incorporates input primitive attributes and contour information in the learning. In addition, new learning branches are added to the network. By learning the user’s personalized performance data in the new branch, the model is more adaptable to different drivers. With reference to the user state by the model perceived, the cockpit switches between different cloud scenario modes. Finally, in comparative experiments, our model shows advantages over other models in driver state recognition.

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An Early Prediction of Diseases via M-L Based Algorithm in Hybrid with the Existing Structure

An Early Prediction of Diseases via M-L Based Algorithm in H...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Mohammed I. Habelalmateen Rajesh Singh Sohini Chowdhury Svsv Prasad Sanaboina Arti Badhoutiya Neelam Sanjeev Kumar The Islamic university Najaf Iraq Department of Electronics & Communication engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Centre of Research Impact and Outreach Chitkara University Rajpura Punjab India Department of CSE CMR Technical Campus Hyderabad Telangana India Department of Electrical Engineering GLA University Mathura Department of Computer Science & Engineering SRM Institute of Science & Technology Vadapalani Chennai

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Due to alterations in the environment and changes in lifestyle, illnesses are becoming more common in the modern day. Many illnesses cannot advance to a more dangerous stage without early discovery and prognosis. However, while personally assessing patients, doctors are prone to errors or discrepancies. thus, developing a trustworthy technique to identify and forecast the incidence of chronic illnesses is the primary goal of this research. To achieve this purpose, we employ cutting-edge machine learning, and we promise precise illness categorization. Regarding the prediction of illness, data mining is crucial. Our suggested method makes use of machine learning techniques like Convolutional Neural Network (CNN) for automated feature extraction and matching to allow precise illness prediction. Our technology provides a wide range of illness prognoses depending on the symptoms of specific patients by analysing this dataset.

关键词： machine learning algorithms machine learning Medical services Prediction algorithms Feature extraction Convolutional neural networks data mining

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A combined data-driven method for electricity theft identification 4

A combined data-driven method for electricity theft identifi...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Li, Qilin Zhang, Zhemin Li, Yun Jin, Wenxing Metering Center of Sichuan Electric Power Corporation Chengdu610045 China College of Computer and Science and Technology Shanghai University of Electric Power Shanghai200090 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781665413220

Electricity theft is one of the important sources of non-technical losses in the power grid, which has been an important problem faced by electricity enterprises. the data collected by the advanced metering infrastructure (AMI) makes it possible to identify electricity theft based on a data-driven method. However, among the data-driven methods, the methods based on supervised learning, unsupervised learning and state estimation have their advantages and disadvantages, which can not play a full role. this paper presents a combined data-driven identification framework, which combines the advantages of the supervised method (day-week-month load convolutional neural network (DWML-CNN)), unsupervised method (clustering by fast search and find of density peaks (CFSFDP)) and state estimation method. Firstly, the features are extracted based on the state estimation, then the unsupervised and supervised methods are used to determine the suspicion ranks respectively. Finally, a comprehensive evaluation is made according to the two suspicion ranks. Experimental results show that the framework has good performance on the data set of the State Grid Corporation of China (SGCC). © 2021 IEEE.

关键词： State estimation

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Medical data Clustering Based on Multi-objective Clustering Algorithm 4th

Medical Data Clustering Based on Multi-objective Clustering ...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Chen, Shilian Tan, Yingsi Guo, Junkai He, Yuqin Geng, Shuang College of Management Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031201011

With the development of massive medical data, clustering algorithm becomes an effective way for medical data processing and data mining. On the one hand, it helps medical learners find effective information patterns from massive data;on the other hand, it promotes the development of medical technology and increase productivity. For traditional clustering algorithm, a single clustering index is difficult to meet people's needs of diversity and comprehensiveness. In contrast, multi-objective clustering (MOC) considers multiple objectives at the same time, and comprehensively deals with various clustering problems and standards, such as compactness, diversity of feature selection and high data dimension. Artificial bee colony algorithm (ABC) has a faster speed and embodies the idea of swarm intelligence. It imitates the optimization process of bees, and finally obtains the global optimal value. On this basis, this paper proposed a multi-objective artificial bee colony clustering algorithm (MOC-NABC) that is combined with current better-performed clustering algorithm. It takes normalized mutual information (NMI), Calinski-Harabasz (CH), Fowlkes-Mallows index (FMI) and silhouette coefficient (SC) of clustering as the final evaluation indexes. the experiment on UCI mouse protein gene dataset shows that the overall performance effect is greatly improved, e.g. compact clustering and the effective utilization of data features. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clustering algorithms

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A Smart System for Sign Language recognition using machine learning Models 4

A Smart System for Sign Language Recognition using Machine L...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Rajalingam, B. Kumar, R. Santhosh Deepan, P. Santosh Kumar Patra, P. Bavankumar, S. St. Martins Engineering College Cse Secunderabad India St. Martins Engineering College AIDS Secunderabad India St. Martins Engineering College AIML Secunderabad India

ISBN: (纸本)9781665474368

When people exchange information, thoughts, or sentiments via the medium of communication, it is called communication. Both parties must be conversant in the same language in order for communication to occur. On the other hand, those who are deaf or dumb must use various communication methods. If you can't hear, you have deafness;if you can't speak, you have dumbness. As a group, they use sign language to communicate, but most people don't see it as a valuable skill. To communicate with someone who is deaf or mute is difficult since not everyone is familiar with or understands sign language. You may develop a model using machine learning to get around this roadblock. It is possible to teach a model to recognize different gestures used in sign language and to translate those gestures into English. Many people will get the ability to converse with the deaf and dumb as a result of this. By using a camera to capture Sign Language data, we may then use machine learning techniques like Convolutional Neural Networks to the datasets. Some other well-known models have been compared to a new strategy that has been presented for the same problem. Principal Component Analysis (PCA), Histogram of Gradients (HOG), and Local Binary patterns (LBP) are some of the pre-processing approaches employed. ORB, Canny edge detection, and the state of word approach are all used to create the new model. this preprocessed data is then sent through a variety of classifiers in order to provide useful findings (including Random Forests and Support Vector machines, as well as Nave Bayes and Logistic Regression). the new models are substantially more accurate than the previous ones. the method achieves good accuracy even with a little dataset. © 2022 IEEE.

关键词： Principal component analysis

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17th international conference on Information Security Practice and Experience, ISPEC 2022

17th International Conference on Information Security Practi...

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17th international conference on Information Security Practice and Experience, ISPEC 2022

ISBN: (纸本)9783031212796

the proceedings contain 35 papers. the special focus in this conference is on Information Security Practice and Experience. the topics include: Sparse Attack on Skeleton-Based Human Action recognition for Internet of Video things Systems;LMGROUP: A Lightweight Multicast Group Key Management for IoT Networks;Using API Calls for Sequence-pattern Feature mining-Based Malware Detection;evaluating the Possibility of Evasion Attacks to machine learning-Based Models for Malicious PowerShell Detection;detect Compiler Inserted Run-time Security Checks in Binary Software;Detection of MSOffice-Embedded Malware: Feature mining and Short- vs. Long-Term Performance;priApp-Install: learning User Privacy Preferences on Mobile Apps’ Installation;BADPASS: Bots Taking ADvantage of Proxy as a Service;A CNN-Based Semi-supervised learning Approach for the Detection of SS7 Attacks;blockchain-Based Self-Sovereign Identity System with Attribute-Based Issuance;Federated Intrusion Detection on Non-IID data for IIoT Networks Using Generative Adversarial Networks and Reinforcement learning;preventing Adversarial Attacks Against Deep learning-Based Intrusion Detection System;detection of Adversarial Examples Based on the Neurons Distribution;techniques for Continuous Touch-Based Authentication;Performance Evaluation of Post-Quantum TLS 1.3 on Resource-Constrained Embedded Systems;secure Human Identification Protocol with Human-Computable Passwords;practical Backdoor Attack Against Speaker recognition System;anonymous Authentication Protocols in Vehicular Ad Hoc Network-An Survey;optimal Generic Attack Against Basic Boneh-Boyen Signatures;Differential Cryptanalysis of Salsa20 Based on Comprehensive Analysis of PNBs;blockchain-Based Confidential Payment System with Controllable Regulation;physical Zero-Knowledge Proof Protocol for Topswops;attribute Based Tracing for Securing Group Signatures Against Centralized Authorities;sherlock Holmes Zero-Knowledge Protocols;Continued Fractions Applied to a

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IoT-Enabled WBAN and machine learning for Emotion recognition

IoT-Enabled WBAN and Machine Learning for Emotion Recognitio...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Hritesh Mathur Harsh Panwar Rahul Karki Vivek Jangra Ramnaresh Yadav Anjali Kapoor Department of Information Technology Amity University Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

All industries, including computer science and health care and customer service are in dire need of efficient recognition of human feelings. this work is a script for a novel approach to affect recognition which involves both vocal emotion analysis and face emotion detection. Voice sentiment analysis project used machine learning technique to identify the prospects of emotional content in voice data. the model also assists in the classification of emotions with reference to the speech emotional states based on added features of MFCC and chroma. On the other hand, face emotion recognition project by applying deep learning algorithms identifies emotions by face expressions. With the help of convolutional, neural networks (CNNs) determining the happiness, sadness, and anger it analyses the facial features correctly. Combining all of these results in a coherent system that allows for the recognition of feelings through voice and or facial signals. Consequently, as demonstrated in the experimental results provided hereinafter, the accuracy of the presented system in correctly identifying human emotions is significantly higher than when using single models on their own. therefore this combination creates space for other uses such as healthcare marketing among other fields hence improving user experience while at the same time providing unforeseen analysis of human conduct during an interaction with intelligent devices.

关键词： Deep learning Wireless communication Emotion recognition Sentiment analysis Accuracy Face recognition Speech recognition Medical services User experience Stability analysis

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