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

4th International Conference on Machine Learning for Cyber S...

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

ISBN: (纸本)9783031200953

the proceedings contain 152 papers. the special focus in this conference is on machine learning for Cyber Security. the topics include: machine learning Based Security Situation Awareness Method for Network Data Transmission Process;multi-objective Hydrologic Cycle Optimization for Integrated Container Terminal Scheduling Problem;High Voltage Power Communication Network Security Early Warning and Monitoring System Based on HMAC Algorithm;Large Scale Network Intrusion Detection Model Based on FS Feature Selection;Privacy Preserving CSI Fingerprint Device-Free Localization;Research on Intelligent Detection Method of Automotive Network Data Security Based on FlexRay/CAN Gateway;adversarial Attack and Defense on Natural Language Processing in Deep learning: A Survey and Perspective;a Novel Security Scheme for Mobile Healthcare in Digital Twin;Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining Data;spiking Neural Networks Subject to Adversarial Attacks in Spiking Domain;Diverse Web APIs Recommendation with Privacy-preservation for Mashup Development;network Security Evaluation Method of College Freshmen Career Counseling Service Based on machine learning;FedTD: Efficiently Share Telemedicine Data with Federated Distillation learning;increase Channel Attention Based on Unet++ Architecture for Medical Images;distributed Power Load Missing Value Forecasting with Privacy Protection;A Novel Blockchain-MEC-Based Near-Domain Medical Resource Sharing Model;differentially Private Generative Model with Ratio-Based Gradient Clipping;differential Privacy Protection Algorithm for Data Clustering Center;improved Kmeans Algorithm Based on Privacy Protection;symmetry Structured Analysis Sparse Coding for Key Frame Extraction;data Reconstruction from Gradient Updates in Federated learning;natural Backdoor Attacks on Speech Recognition Models;boarding Pass Positioning with Jointly Multi-channel Segmentation and Perspective Transformation Co

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

4th International Conference on Machine Learning for Cyber S...

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

ISBN: (纸本)9783031201011

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machine learning Techniques for Autism Spectrum Disorder: Current trends and future directions 4

Machine Learning Techniques for Autism Spectrum Disorder: Cu...

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4th international conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Khan, Kainat Katarya, Rahul Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665494144

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. this disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. this paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. the presented research work can remarkably aid future studies on machine learning for ASD. © 2023 IEEE.

关键词： Supervised learning

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11th international conference on Analysis of Images, Social Networks and Texts , AIST 2023

11th International Conference on Analysis of Images, Social ...

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11th international conference on Analysis of Images, Social Networks and Texts , AIST 2023

ISBN: (纸本)9783031670077

the proceedings contain 22 papers. the special focus in this conference is on Analysis of Images, Social Networks and Texts. the topics include: machine Translation Models Stand Strong in the Face of Adversarial Attacks;Whether Large Language Models Learn at the Inference Stage? Effects of Active learning and Labelling with LLMs on their Reasoning;machine Translation for Russian-Khakas Language Pair: Translation Results in Low-Resource Setting;automatic Aspect Extraction from Scientific Texts;user Review Summarization in Russian;tuning-Free Discriminative Nearest Neighbor Few-Shot Intent Detection via Consecutive Knowledge Transfer;prompt-Tuning for Targeted Sentiment Analysis in Russian;on the Way to Controllable Text Summarization in Russian;document-Level Relation Extraction in Russian;the Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends;interactive Image Segmentation with Superpixel Propagation;gesture Recognition on Video Data;Semantic-Aware GAN Manipulations for Human Face Editing;learning Facial Expression Recognition In-the-Wild from Synthetic Data Based on an Ensemble of Lightweight Neural Networks;acne Recognition: Training Models with Experts;application of Multimodal machine learning for Image Recommendation Systems;date-Driven Approach for Identifying State of Hemodialysis Fistulas: Entropy-Complexity and Formal Concept Analysis;Approximate Density Computation for OA-Biclustering;distributed Bayesian Coresets;development of a Visualization Tool for the Occurrence of Life Events on the Demographic Lexis Grid.

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Wordle result prediction based on machine learning algorithm LSTM model 4

Wordle result prediction based on machine learning algorithm...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Zhang, Zechuan Beijing University of Technology Beijing China

ISBN: (纸本)9798350342710

Wordle, a novel word puzzle courtesy of the New York Times, is becoming popular on the Internet. Since players are willing to share their scores on Twitter, this adds a social attribute to the game. We first conducted a multi-factor ANOVA on the attributes of the words and the percentage of scores in the difficult mode. the results showed that the letters in the first four positions of the word and the number of repeated letters in the word had an effect on the percentage of scores. Next, we proposed a multiple-input-multiple-output long and short-term memory (LSTM) model to predict the distribution of reported outcomes. To incorporate the internal effects of the data into the study, we designed a six-input-seven-output LSTM regression prediction model and trained it with the dataset. then, the trained network was used to predict the distribution of "EERIE"scores. To predict the difficulty of words, a random forest model was designed to determine which of these attributes were associated with each classification by using several attributes carefully selected from the words. Finally, the difficulty of the word "EERIE"was predicted. © 2023 IEEE.

关键词： Forecasting

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Heart Disease Predictive Analysis Using machine learning Approaches 6

Heart Disease Predictive Analysis Using Machine Learning App...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Rana, Anamika Malik, Sushma Chauhan, Madhu Kumar, Prashant Maharaja Surajmal Institute Department of Computer Application New Delhi India Institute of Innovation in Technology & Management Department of Computer Application New Delhi India Jagannath International Management School New Delhi India

ISBN: (纸本)9798350304480

machine learning (ML) has found widespread applications in the healthcare sector worldwide, including the diagnosis and treatment of heart diseases, locomotor disorders, and various other medical conditions. ML methods have revolutionized healthcare by enabling the analysis of large and complex medical datasets, leading to valuable insights and predictions that aid healthcare professionals in providing better patient care. ML's ability to analyze vast amounts of healthcare data, uncover patterns, and make predictions has significant potential to improve patient outcomes, optimize medical workflows, and advance medical research. However, it's essential to address privacy and ethical considerations when using ML in healthcare, ensuring the responsible and secure use of sensitive patient information. Supervised learning methods like SVM, Random Forest, and Logistic Regression are used for the analysis of the dataset downloaded from Kaggle. the various performance parameters such as Precision, F-1 score, Accuracy, and Recall were used to compare the performance of different ML classification techniques. Among the various methods evaluated, the Random Forest classification algorithm was found to outperform the other methods across the fourteen available parameters. © 2023 IEEE.

关键词： Heart Disease Logistic Regression machine learning (ML) Random Forest SVM

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9th EAI international conference on Smart Objects and Technologies for Social Goods, GOODTECHS 2023

9th EAI International Conference on Smart Objects and Techno...

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9th EAI international conference on Smart Objects and Technologies for Social Goods, GOODTECHS 2023

ISBN: (纸本)9783031525230

the proceedings contain 13 papers. the special focus in this conference is on Smart Objects and Technologies for Social Goods. the topics include: Enhancing Student Well-Being: the Impact of a Mindfulness-Based Program (Mind7 +) on Stress, Anxiety, Sleep Quality, and Social Interactions in a Sample of Portuguese University Students;Algorithm for Diagnosis of Metabolic Syndrome and Heart Failure Using CPET Biosignals via SVM and Wavelet Transforms;meal Suggestions for Caregivers and Indecisive Individuals Without a Set Food Plan;the Impact of the Evolution of Operating Systems on Older Web applications;Method for Evaluating the Performance of Web-Based APIs;application of Traditional and Deep learning Algorithms in Sentiment Analysis of Global Warming Tweets;3D Simulation and Comparative Analysis of Immune System Cell Micro-Level Responses in Virtual Reality and Mixed Reality Environments;developing a 3D Laparoscopy Training application to Assess the Efficacy in Virtual Reality Environments;collaborating with Agents in Architectural Design and Decision-Making Process: Top-Down and Bottom-Up Case Studies Using Reinforcement learning;5G and IoT for Intelligent Healthcare: AI and machine learning Approaches—A Review;experience of Visually-Impaired Children in the Enjoyment of Cartoons.

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Research and application of a Tourism Recommendation System Based on Emotional Analysis 4

Research and Application of a Tourism Recommendation System ...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Yang, Wenmiao Wei, Dongmei Huang, Wenjin Huang, Xiaoou School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798400709449

By using convolutional neural network models, it is possible to quickly and accurately recommend potential tourist attractions and routes of interest to users based on their interests and behaviors, thereby significantly improving the user experience. this article is based on a convolutional neural network model and develops a system that can recommend suitable tourist attractions through users uploading videos and capturing facial images for intelligent sentiment analysis. the experimental results show that the accuracy of the model reaches 83.3%, proving that it can accurately recognize user emotions and provide a more suitable travel recommendation scheme. In the post pandemic era, this system has extremely important practical significance in alleviating social anxiety and relieving people's daily depression. © 2023 ACM.

关键词： Neural network models

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Deep learning Algorithms for Timelapse Image Sequence-Based Automated Blastocyst Quality Detection 4

Deep Learning Algorithms for Timelapse Image Sequence-Based ...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Eswaran, Malathi Balasubramanie, P. Mohammed Salman, J. Kongu Engineering College Department of Computer Technology Erode India

ISBN: (纸本)9798350300093

the blastocyst is a hollow cellular mass that forms during early development, comprising of cells that make up the trophectoderm, inner cell mass, and a fluidfilled cavity called the blastocoel. In vitro fertilization (IVF) is a fertilization process where eggs are combined with sperm outside the body. this involves closely monitoring and stimulating the ovulation process, extracting eggs from the ovaries, and allowing them to be fertilized by sperm in a laboratory culture medium. In the current system, a STEM model is utilized for time-lapse monitoring of the embryos. three embryologists with a combined experience of at least five years review the results, which are subsequently split into a training dataset (80%) and a validation dataset (20%). Unusable blastocysts are embryos that fail to form into blastocysts or display subpar blastocysts. In proposed system, InceptionV3 is one of the best predefined models that suits for blastocyst quality deduction. the model's performance is evaluated using error rate and accuracy. In this research paper, InceptionV3 is applied and achieved a significantly higher accuracy of 95% in of blastocyst quality deduction. © 2023 IEEE.

关键词： Deep learning

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Research on instrument fault detection based on internet of things and machine learning 4

Research on instrument fault detection based on internet of ...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Li, Shixi Zhao, Chenchen Yang, Benchen Peng, Zihang Bi, Jiayi School of Software Liaoning University of Technology Liaoning Huludao China Liaoning Provincial Second Women's Prison Liaoning Shenyang China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Modern industrial production, the industrial sector and daily life are inextricably linked to a plethora of chemical machinery and equipment, which serve as indispensable production apparatus. It is therefore paramount to guarantee their optimal functionality. It is therefore of great significance to the field of chemical machinery and equipment that fault monitoring is given due attention. the progressive advancement of computer and wireless communication technology has facilitated the maturation of expert diagnosis systems and the Internet of things. Consequently, fault monitoring systems based on these technologies are poised for widespread adoption. In this work, the instrument operation data collected by IoT devices is used to undergo preliminary cleaning and normalization to ensure the consistency and accuracy of the data. this preprocessed data is then fed into a multi-layered deep neural network (DNN) model. In the model training stage, the backpropagation algorithm is used to calculate the gradient of each layer, and the weights and biases in the network are adjusted by the stochastic gradient descent (SGD) optimization algorithm to minimize the loss function. through iterative training, the model gradually improves its ability to identify failure modes and predict accuracy. this approach enables deep neural networks to automatically learn complex fault signatures from large amounts of data and effectively detect and predict instrument failures in real-world applications. An experimental platform was set up in the LabView programming environment, equipped with 10 sensors, which were used to collect experimental data from the sensors and conduct fault monitoring experiments. the model shows high accuracy and robustness in different types of instrument fault detection, proving its effectiveness in practical industrial applications. © 2025 SPIE.

关键词： Deep neural networks

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