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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Comparative Analysis of Suspicious Activity Detection Techniques in Surveillance Videos

Comparative Analysis of Suspicious Activity Detection Techni...

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Disruptive Technologies (ICDT), international conference on

作者： Prabhakar Kumar Sahil Singh Md. Tahseen Raza Yojna Arora Department of Computer Science and Technology Sharda University Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The need to detect suspicious activities automatically in surveillance videos has become a significant challenge in recent time and became a hot topic for researchers to find and develop new and better techniques to solve this problem. This paper provides a detailed review of several papers and the methods used in those on anomaly detection in video surveillance using different machine learning and deep learning techniques which have been explored till now and gave promising results. CNN-based models, spatiotemporal autoencoders, object-centric autoencoders, and transfer learning-based algorithms are among the approaches examined in this paper. A comparative review of their techniques, accuracy, computing efficiency, and real-time application is presented.

关键词： Deep learning machine learning algorithms Reviews Computational modeling Autoencoders Video surveillance Real-time systems Spatiotemporal phenomena Disruptive technologies Videos

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3rd international conference on 3D Imaging Technologies - Multidimensional Signal Processing and Deep learning, 3DIT-MSP and DL 2021

3rd International conference on 3D Imaging Technologies - Mu...

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3rd international conference on 3D Imaging Technologies - Multidimensional Signal Processing and Deep learning, 3DIT-MSP and DL 2021

ISBN: (纸本)9789811924514

The proceedings contain 23 papers. The special focus in this conference is on 3D Imaging Technologies - Multidimensional Signal Processing and Deep learning. The topics include: Prediction of Silicon Content in Molten Iron Based on EMD-GA-LSTM;using Meta Path Information to Adjust Embedded Recommendation System;intelligent Campus Blog Preprocessing Model;an Experimental Study on Extraction Method of Tobacco Color Distribution Feature;research on the Distribution Map of Weeds in Rice Field Based on SegNet;preface;study on Remote Sensing Retrieval of Land Surface Evapotranspiration in Poyang Lake Region in Typical Hydrological Year;research on the application of Artificial Intelligence in Unstructured Data Resource Management of Power News Information;analysis and application of Medical Images in the Field of Artificial Intelligence;development and Design of Surface Quality Online Inspection System Based on machine Vision;terahertz Surface-Plasmon-Polaritons Gradient Index Lens;Improvement of YOLOv4 Algorithm and Its application in Objective Identification of Railway Level Crossing;design of Automatic Verification System for Electronic Balance Based on machine Vision;research on Interactive Art Design System Based on Artificial Intelligence Technology;infrared Imaging Circuit Multi-Channel Analog-To-Digital Sampling Circuit Inconsistency Correction;research on Artificial Intelligence System Design of Ideological and Political Education;research on 3D Virtual Emotional Interaction Design Based on Network Digitization;research on Image Quality Assessment of Vehicle Panoramic View Equipment;research and Implementation of Digital Art Media System Based on Big Data Aesthetics;Research on application of OpenGL-Based Game Design;application of Artificial Intelligence Technology in Marketing.

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A Review of Healthcare Recommendation Systems Using Several Categories of Filtering and machine learning-Based Methods 3

A Review of Healthcare Recommendation Systems Using Several ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Kumar, Pardeep Kumar, Ankit Baba Mastnath University Asthal Bohar Rohtak India

ISBN: (纸本)9781665462006

Health-care Recommendation Systems (RSs) is a famous application of AI (artificial intelligence) that involves the investigators worldwide. Several ML methods are utilized to develop health-care RSs. Selecting the best ML method to give customers a service/product is an interesting task in health-care RSs. Currently, it is observed in the buying pattern of individuals from in-shop to online the outcome in accessibility of OI (online information) which is exponentially improved day by day. In shopping scenarios, these RSs must be capable of advising reliable solutions to the customers. The medical health-care RSs have to manage a large quantity of data by cleaning the reliable data depending on the data analysis, generated on the user behaviour made by the customers during their online terms. RSs can suggest suitable items to customers based on their interests and existing favourites, leading to improved sales. This paper described the overview of health-care RS, phases of RSs such as IC (information collection), learning, and recommender or predictor. This paper presents an overview of three main methods used to construct health-care RSs that are content-based, collaborative filtering (CF)-based and hybrid (HB)-based filtering methods and discuss several ML-based methods used in health-care RSs with their performance comparison. The ML-based methods include;MFM (matrix factorization model), CNN, MLP, SVD, etc. © 2022 IEEE.

关键词： Health care

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QUARK: A Framework for Quantum Computing application Benchmarking 3

QUARK: A Framework for Quantum Computing Application Benchma...

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3rd IEEE international conference on Quantum Computing and Engineering (QCE)

作者： Finzgar, Jernej Rudi Ross, Philipp Hoelscher, Leonhard Klepsch, Johannes Luckow, Andre BMW Grp Munich Germany Tech Univ Munich Munich Germany Ludwig Maximilian Univ Munich Munich Germany

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

Quantum computing (QC) is anticipated to provide a speedup over classical approaches for specific problems in optimization, simulation, and machine learning. With the advances in quantum computing toward practical applications, the need to analyze and compare different quantum solutions is increasing. While different low-level benchmarks exist, they often do not provide sufficient insights into real-world application-level performance. We propose an application-centric benchmark method and the QUantum computing application benchmaRK (QUARK) framework to foster the investigation and creation of application benchmarks for QC. This paper establishes three significant contributions: (1) it makes a case for application-level benchmarks and provides an in-depth "pen and paper" benchmark formulation of two reference problems: robot path and vehicle option optimization from the industrial domain;(2) it proposes the open-source QUARK framework for designing, implementing, executing, and analyzing benchmarks;(3) it provides multiple reference implementations for these two reference problems based on different known, and where needed, extended, classical and quantum algorithmic approaches and analyzes their performance on different types of infrastructures.

关键词： quantum computing benchmark optimization

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

3rd International Conference on Artificial Intelligence and ...

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

ISBN: (纸本)9783030957100

The proceedings contain 54 papers. The special focus in this conference is on Artificial Intelligence and Speech Technology. The topics include: Feature Extraction and Sentiment Analysis Using machine learning;a Neural Network Based Approach for Text-Level Sentiment Analysis Using Sentiment Lexicons;cross Linguistic Acoustic Phonetic Study of Punjabi and Indian English;prashn: University Voice Assistant;spectrogram Analysis and Text Conversion of Sound Signal for Query Generation to Give Input to Audio Input Device;a Contrastive View of Vowel Phoneme Assessment of Hindi, Indian English and American English Speakers;Text-Based Analysis of COVID-19 Comments Using Natural Language Processing;indian Languages Requirements for String Search/comparison on Web;dictionary Vectorized Hashing of Emotional Recognition of Text in Mutual Conversation;Speaker Independent Accent Based Speech Recognition for Malayalam Isolated Words: An LSTM-RNN Approach;Comparative Analysis of NLP Text Embedding Techniques with Neural Network Layered Architecture on Online Movie Reviews;current State of Speech Emotion Dataset-National and international Level;context-Aware Emoji Prediction Using Deep learning;development of ManiTo: A Manipuri Tonal Contrast Dataset;deep Neural Networks for Spoken Language Identification in Short Utterances;a Lightweight Deep learning Approach for Diabetic Retinopathy Classification;machine Hearing a Cognitive Service for Aiding Clinical Diagnosis;critical Insights on Cancer Detection Using Deep learning;OHF: An Ontology Based Framework for Healthcare;enhancing the Deep learning-Based Breast Tumor Classification Using Multiple Imaging Modalities: A Conceptual Model;a Review on Speech Synthesis Based on machine learning;EEG Based Stress Classification in Response to Stress Stimulus;hindi Phoneme Recognition - A Review.

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A comparative research on the effectiveness of two mobile applications for vocabulary learning: BaiCiZhan and Bubeidanci 3

A comparative research on the effectiveness of two mobile ap...

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Zhang, Peirao Department of English Language Education The Education University of Hong Kong Hong Kong

ISBN: (纸本)9781510661240

In recent years, with the development of science and technology, more and more language teachers and researchers pay attention to the application of mobile devices in language learning. However, few studies have explored the effectiveness of specific vocabulary learning applications in the Chinese context and made suggestions for students' choices. This research involves 16 participants using two popular Apps named BaiCiZhan and Bubeidanci respectively. They were required to do a pre-test, two immediate post-tests and a delayed post-test, to compare the difference in the effect of vocabulary retention of the two apps. The results showed that although both apps significantly improved the retention effect immediately after learning and one week after learning, there was no difference between the two apps. In addition, interviews were conducted to investigate the participants' experience of using the two apps and combining the vocabulary learning strategies used in two apps. This study concluded that the learning style preference of learners could be seen as a factor, and the two most important vocabulary learning strategies respectively used by the two apps which are using pictures and using various context sentences both have their own advantages and disadvantages, which makes no difference in the final result of word retention between the two apps. © 2022 SPIE.

关键词： learning systems

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Detection of Electricity Theft using a Deep learning-based Bayesian optimization algorithm and LSTM 3

Detection of Electricity Theft using a Deep Learning-based B...

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3rd international conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Dimf, G.P. Kumar, P. Manju, V.N. Centre for Information Technology and Engineering Manonmaniam Sundaranar University Tamilnadu Tirunelveli627012 India CMR Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474979

Theft of electricity is one of the complex issues facing advanced technologies. Due to ET, the world's electric utilities would suffer significant financial losses. The standard electricity theft detection (ETD) models face a number of difficulties, including the dimensionality curse, the unavoidable effects of benign factors, and the very unbalanced distribution of electricity consumption data. In order to identify electricity theft, we propose an LSTM (long short-term model) based on deep learning. We suggest a deep learning-based electricity theft detector in this work to identify theft attempts. Deep learning techniques are suggested by combining two separate phases, such as data pre-processing and classification, in order to detect electricity theft from user power usage patterns. The initial phase in the process of refining data entails standardizing and addressing classes that are unbalanced. The second step is feature extraction, where we employ the Long Short-Term Memory (LSTM) block to more effectively extract and memorise features from vast time series data. This effectively extracts important information to accurately portray occurrences of power theft. The third phase involves classifying the data using the deep learning-based Bayesian optimization algorithm (DL-BOA), which also saves and extracts the long-term dependency in the data on power use. The suggested framework for deep learning greatly surpasses earlier machine learning techniques like logistic regression and SVM in terms of accuracy, according to simulation findings. The simulations are run to demonstrate how well our suggested model works. Information to track down electricity thieves and stop the big money losses in the power industry. © 2022 IEEE.

关键词： Deep learning Support vector machines Time series analysis Feature extraction Power industry Bayes methods Classification algorithms

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Model of Information System for application of Blockchain Technologies 3

Model of Information System for Application of Blockchain Te...

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2021 3rd international conference on computer, Communications and Mechatronics Engineering, CCME 2021

作者： Ishchukova, Evgeniya Romanenko, Kirill Salmanov, Vyacheslav Department of Information Security Southern Federal University Russia

This article describes the current security problems of blockchain networks, identifying the least explored area. The concept of an information system for investigating blockchain incidents is presented. An example of stealing bitcoins from a cryptocurrency wallet was taken as input. This example contains a fairly large number of blocks and transactions, and everything needs to be studied. An overview of the existing vulnerabilities in the Ethereum blockchain platform is presented. A brief description of the basic principles of operation of two largest cryptocurrency platforms - Bitcoin and Ether - is given. Examples of possible incidents that are widespread among users of blockchain systems are considered. The architecture of the designed solutions is described. The first experimental results obtained on the basis of the implementation of the designed algorithms are presented. It is concluded that iterating over a large number of transactions takes a lot of time;in the future, it is planned to apply machine learning methods to analyze such a large data array. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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CorefPM: Coreference Resolution as Linguistic Prompt Prediction and machine Reading Comprehension 22

CorefPM: Coreference Resolution as Linguistic Prompt Predict...

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3rd international conference on Control, Robotics and Intelligent System, CCRIS 2022

作者： Zhao, Jianxuan Wang, Huazhen Zhao, Yifei College of Computer Science and Technology Huaqiao University Xiamen361021 China College of Preparatory Education Huaqiao University Xiamen361021 China

ISBN: (纸本)9781450396851

Coreference resolution is a key challenge in natural language processing and is considered as a good test of machine intelligence. Recent coreference resolution prefers to supervised methods due to its superior performance. But supervised methods suffer from data scarcity and limited generalizability. In this paper, we present CorefPM, an unsupervised approach for the coreference resolution task. We formulate the task as following steps: (1) We generate the candidate mention set by linguistic knowledge and score them by prompt prediction method.(2) We obtain another mention with its confidence score by machine reading comprehension(MRC) model.(3) We output final resolution result by comparing candidate mention and MRC mention. Experiments demonstrate that our approach significantly outperforms the baseline of few-shot and zero-shot in ZeroCLUE, although performs worse on CLUEWSC 2020 dataset than supervised learning baselines. Our experiment results suggested that linguistic knowledge is valuable and can improve the result of coreference resolution task. © 2022 ACM.

关键词： Supervised learning

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Synergistic Integration of Blockchain Technology and machine learning Algorithms for Optimization of Agriculture Supply Chain Management

Synergistic Integration of Blockchain Technology and Machine...

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

作者： Gayatri Asalkar Prema Sahane Atharv Pedde Samarth Hanse Tejas Ghondage Krishna Bhosale Department Of Computer Engineering JSPM's Rajarshi Shahu College of Engineering Pune Maharashtra India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research study focuses on the discussion of blockchain and machine learning optimization in the conventional agricultural supply chain. Optimization of both rising technologies has the possibility of addressing some of the complex and entrenched issues that confront the agricultural industry while receiving calls for more openness, honesty, and productivity. Blockchain, being the technological application that is based on secure and transparent transactions and enables the assurance of immutability and machine learning as a data driven power in optimization, leads to a significantly better traceability, erased inefficiencies, less substances and time to market. It is however not entirely without some few challenges that will have to be overcome for all these innovations to come into their own. Among the obstacles that need to be overcome for realization of these innovations some of the critical ones are Technology and Data issues and Stakeholder issues.

关键词： Technological innovation machine learning algorithms Supply chains Time to market machine learning Agriculture Blockchains Stakeholders Reliability Optimization

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