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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Opinion mining from Audio Conversation using machine learning tools and BART Transformer

Opinion Mining from Audio Conversation using Machine Learnin...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Ann Mathews Paras Nath Singh Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India

The discipline of opinion mining is expanding rapidly. It gathers subjective data from text and audio sources using Natural Language Processing and text analysis. Finding and extracting subjective data automatically from news stories, blogs, social media posts, customer evaluations, and other sources is the primary objective of opinion mining. Opinions, assessments, appraisals, sentiments, and emotions fall within this category. As consumers increase and data accumulates daily, companies rely on data collection and analytics to track sales, customer insights, and brand reputation. However, voice data, the most direct feedback, is often overlooked. This paper explains how to analyse the sentiment in audio recordings and apply the top three approaches in three different groups of opinions (positive, negative, and neutral) in order to gain a better understanding of how customers evaluate goods and services. An extensive examination of benchmarked datasets, popular feature sets, algorithms, approaches, open-source tools, difficulties, practical applications, and insights into several facets of opinion mining are provided in this research. With 99%+ accuracy for various data sets using Python's BART transformer, these results have both theoretical and practical implications, as well as demonstrating the utility of opinion mining from sound to text converted information in a holistic society.

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machine learning Based pattern recognition with HOG Features for Detection of Islanding

Machine Learning Based Pattern Recognition with HOG Features...

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international conference on Electrical Power and Energy Systems (ICEPES)

作者： Kumaresh Pal Subhadip Goswami Anshuman Bhuyan Santanu Koley Bidisha Bera Sarmistha Basu Department of Electrical Engineering National Institute of Technology Jamshedpur India Department of Electrical Engineering National Institute of Technology Arunachal Pradesh India Department of Electrical Engineering Gandhi Institute for Education & Technology Bhubaneswar India Department of Computer Science Engineering Haldia Institute of Technology Haldia India Department of Computer Science and Technology Global Institute of Science and Technology Haldia India Department of Physics Calcutta Institute of Engineering and Management Kolkata India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a machine learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds.

关键词： Histograms Accuracy Voltage Feature extraction Vectors pattern recognition Classification algorithms

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Proceedings - 2021 3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

Proceedings - 2021 3rd International Conference on Advances ...

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3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

ISBN: (纸本)9781665438117

The proceedings contain 403 papers. The topics discussed include: a modified data clustering algorithm for uncertain data in peer to peer networks;concept drift detection for social media: a survey;voice feature analysis for speaker recognition to improve content management;analysis and prediction of drugs using machine learning techniques;text summarization using extractive techniques;an analysis and research on sentiments of twitter data;advancements of lab on chip in reducing human intervention: a study;an open air outdoor environment for children in urban residential precinct;analysis of online media content using data science techniques;a machine learning approach for predicting dengue outbreak;analysis of student performance and requirement using data science;analytical study of stochastic trends of non-performing assets of public and private commercial banks in india;a critical analysis of diabetes detection using machine learning algorithms;and recommendation system with exploratory data analytics using machine learning.

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Third international conference on Computer Communication and Network Security, CCNS 2022

Third International Conference on Computer Communication and...

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3rd international conference on Computer Communication and Network Security, CCNS 2022

ISBN: (纸本)9781510660113

The proceedings contain 51 papers. The topics discussed include: intrusion detection based on deep belief network and stacking method;the acquisition system incorporating inaccurate replacement under the information technology;an intelligent assessment method of power information intrusion tolerance based on machine learning;research on the fusion scheduling of functional safety and information security of relay protection device based on priority;the construction of legal education network database;anti leakage method of power information communication data based on chaotic mapping;research on security protection path of information infrastructure: how to identify the critical information infrastructure of the communication industry;edge network intrusion detection based on multi-center incremental clustering algorithm;cloud computing security and its application in power grid;and optimization of intelligent supply chain of metering materials based on data mining technology.

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Analyzing the Application of SMOTE on machine learning Classifiers

Analyzing the Application of SMOTE on Machine Learning Class...

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3rd IEEE international conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Rattan, Vikas Mittal, Ruchi Singh, Jaiteg Malik, Varun Chitkara Univ Inst Engn & Technol Chandigarh Punjab India

ISBN: (纸本)9781728185194

data mining functionalities are used in almost every sector including education. Due to continuous evolving information technology and decreasing cost of hardware, every educational institute has an urge to keep the students as much as possible data, in their databases to mine unknown insights. These unknown insights make institutes more proactive and counter reactive and helps them to induce more quality to their teaching and learning processes. In this paper, supervised machine learning techniques are used to predict the student performance using classification. Rapid miner 9.6, leading data science platform, is used for comparative analysis of predictions made by k nearest neighbor, naive bayes, chi square automatic interaction detection decision tree, and random forest using synthetic minority over-sampling technique.

关键词： Educational data mining SMOTE Feature Selection

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ClipXpert: Automated Clip mining from Video data for High-Demand Content

ClipXpert: Automated Clip Mining from Video Data for High-De...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Rajdeep Chatterjee Sudip Chakrabarty Pappu Bishwas School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

ClipXpert is a new system that automates the extraction of relevant clips from YouTube videos using user-defined keywords or frequently used word or comment analysis. The system uses advanced transcription models to search a pre-existing database for lines associated with the provided keywords. If the database doesn’t contain the keywords, it dynamically identifies and stores relevant content for future use. ClipXpert also automates identifying frequently used nouns, adjectives, and adverbs to generate focused content highlights without user intervention. It also performs sentiment analysis on YouTube comments to understand audience engagement and reception. ClipXpert enhances video content extraction efficiency and accuracy, catering to the growing demand for targeted, high-value clips in the digital media landscape.

关键词： Sentiment analysis Video on demand Accuracy machine learning Media Real-time systems Web sites data mining Intelligent systems Videos

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Recyclable and Non-Recyclable Waste recognition using Image Processing with machine learning: Recycle Mate

Recyclable and Non-Recyclable Waste Recognition using Image ...

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Intelligent Technologies (CONIT), international conference on

作者： Amit Nara Smit Panchal Prof. Suchita Dange Department of Computer Engineering A.P. Shah Institute of Technology Thane India

Waste Management is the effort needed to make our world a better place. As solid waste may cause a problem for our surroundings. Nowadays many people don't even know how to distinguish between recyclable and non-recyclable objects, we know only some general things which are recyclable like paper, plastic, cardboard and drinking cans. To deal with this problem a deep machine-learning Convolution Neural Network (CNN) was created to help us all with an achievable waste management task. The automated task works for everyone as it lets down the burden of work from many people’s shoulders. Our model is built using the Keras API. The model categorizes the data into a binary class. It has been tested with many objects which gave an acceptable prediction accuracy.

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Object Detection in Images Using machine learning

Object Detection in Images Using Machine Learning

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： G. Nagarajan Abhiram Piratla Karthikeya Golla Department of CSE Sathyabama institute of science and technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This study develops a scalable, effective, and user-friendly solution to tackle the problem of real-time object detection in photos. The suggested approach makes use of YOLOv5 (You Only Look Once version 5) for deep learning-based object detection and Flask for managing REST API queries. Users can submit base64-encoded photos to the system, which will analyze them using the YOLOv5 model to identify and categorize objects, label them with bounding boxes, and then return the base64-formatted images. High-speed performance is ensured by using PyTorch for model inference, while Non-Maximum Suppression (NMS) increases object localization accuracy by removing redundant detections. Because of its lightweight architecture and ease of integration with online applications, this system can be used for a variety of purposes, including surveillance and e-commerce. The study's output is a reliable, approachable object detection API that can effectively manage real-time image processing and deliver precise item recognition, showcasing the potential for implementing deep learning models in intuitive and scalable web-based settings.

关键词： YOLO Deep learning Accuracy Surveillance Medical services Streaming media Real-time systems Object recognition Reliability Streams

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A data mining Technology of Electricity Trading data Based on data Sandbox

A Data Mining Technology of Electricity Trading Data Based o...

引用

Electronic Technology, Communication and Information (ICETCI), IEEE international conference on

作者： Rui Zhou Xin Hu Dejun Xiang Kangqian Huang Siyu Liu Lingfei Ma Guangdong Electricity Exchange Center Co. Ltd Guangzhou China School of Life Science Beijing Institute of Technology Beijing China School of Science and Technology The Chinese University of HongKong Shenzhen Shenzhen China

With the rapid rise of data mining technology such as machine learning, the flow of data production factors has become a key issue. Due to the high added value and high sensitivity, the mining and utilization of electricity trading data needs security protection. This paper analyzes the value increment of electricity trading data, the security risks and limitations in current applications. Furthermore, this paper proposes a new data mining scheme for electricity trading. Technically, this paper divides the mining tasks for electricity trading data into three categories, and designs a subsystem module for each category of task. Compared with the traditional scheme, this scheme can ensure that users can complete data mining tasks without touching the real data, such that it can support the opening and utilization of any electricity trading data. Compared with the traditional scheme, this scheme greatly increases the scope of data opening. Compared with the privacy protection technology such as secure multi-party computing, our scheme only requires plaintext operations on electricity trading data, and does not require complex ciphertext computation, and hence the computational and communication costs are significantly reduced.

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A Semantic machine learning Algorithm for Cyber Threat Detection and Monitoring Security 3

A Semantic Machine Learning Algorithm for Cyber Threat Detec...

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3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Kumar, Sunil Singh, Bhanu Pratap Kumar, Vinesh AURO University School of IT Surat India Chandigarh University CSE UIE Mohali India

ISBN: (纸本)9781665438117

Due to the enormous increase in number of threats on cyber security, Companies are struggling for advanced mining techniques on data so as to evaluate security logs that IT infrastructures passes on to the organization to make sure efficient and automatically detecting cyber threats. The digital global communities exchanges secured information which explains the precautions that are developed to verify the existence and combination of exchanged information. The next emerging trend in machine learning (ML) analytics are the basis for cyber security is maintaining confidentiality of the mining data security. security log analytics selects machine learning algorithm is a hinder factor for obtaining the achieving of data science in cyber security as there is difficulty in presence of huge number of false detections, specifically during the usage of large-scale or global Security Operations Center (SOC) environments. To observe various cyber-threats number of prior safety structures have been fixed, primary usage of processed data by gadget or warnings for presenting more accurate and precise styles. In this paper, we proposed a optimal machine learning algorithm for executing the framework is basing on analytical classification of observed results, by making use of different prediction, categorization and forecasting algorithm techniques. © 2021 IEEE.

关键词： Cybersecurity

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