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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Stress Detection During Social Interactions with Natural Language Processing and machine learning 4

Stress Detection During Social Interactions with Natural Lan...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Singh, Manish Kumar Kumar, Sunil University Institute of Sciences Chandigarh University Department of Mathematics Gharuan Punjab Mohali India

ISBN: (纸本)9798350385793

the field of Natural Language Processing (NLP) is witnessing a growing recognition of the significance of investigating stress-related phenomena. this research study proposes a new framework to address the diagnostic challenges associated with mental health conditions such as anxiety, depression, and stress, which can worsen if left unaddressed. Within this research study, social media postings and comments are used to enhance sentiment and emotion analysis for assessing individuals' stress levels. Specifically, text analysis of social media data, including Twitter tweets, serves as a pivotal application. the proposed system is implemented by employing a combination of machine learning (ML), Natural Language Processing (NLP), and Deep learning (DL) methodologies. the proposed system utilizes Convolutional Neural Network (CNN), BERT transfer learning techniques, as well as machine learning (ML) algorithms such as Naive Bayes, Support Vector machine (SVM), and K-Nearest Neighbors (KNN). this study aims to predict five distinct emotional categories: 'Happy, ' 'Angry, ' 'Depression, ' 'Sad, ' and 'Fear.' Remarkably, the proposed system has achieved an accuracy and F1 score exceeding 93%, outperforming the performance of other existing human emotion recognition systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting Fruit Diseases Using Deep learning and Image Analysis 4

Detecting Fruit Diseases Using Deep Learning and Image Analy...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Pareek, Tushar P Dobariya, Deep Mahajan, Tanay Bhise, Dhiraj SVKM's NMIMS MPSTME Department of Information Technology Shirpur India

ISBN: (纸本)9798350306118

the implementation of several agriculture-related issues was made simple by advancements in computer vision technology. the detection of fruit diseases is one such issue. Using deep learning techniques, a lot of study has been done on many fruits, including apples, mangos, kiwis, passion fruit, and others. the most significant contributions made by this field in recent years are outlined in this review paper. In this review, we have performed a technical analysis of deep learning methods for predicting fruit illnesses. Along with the deep learning models utilized, the study also compares various picture acquisition, image pre-processing, and segmentation methods. the study discovered that the most accurate deep learning model can vary based on the system's computing capacity and the data used. In the article directions for future research have also been covered. © 2023 IEEE.

关键词： Fruits

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Deep learning for Malware Detection and Classification 4

Deep Learning for Malware Detection and Classification

引用

4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Avhad, Parimal Kolse, Shivam Pangavhane, Parth Gadekar, Tushar Sangale, Abhimanyu Sanjivani College of Engineering Kopargaon Affiliated to Savitribai Phule Pune University Electronics and Computer Engineering Maharashtra Pune India Sanjivani College of Engineering Kopargaon Affiliated to Savitribai Phule Pune University Computer Science Engineering Maharashtra Pune India

ISBN: (纸本)9798350393248

Malicious software, or malware, poses a persistent and evolving threat to cybersecurity. Traditional detection methods based on signatures and heuristics are challenged by the rapid proliferation of polymorphic and zero-day attacks. this research paper explores the application of deep learning, specifically Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in the realm of malware detection and classification. CNNs excel in capturing spatial patterns in binary data, treating it akin to images, while RNNs are adept at handling sequential information. through rigorous experimentation and evaluation, this study provides a comparative analysis of the performance of these deep learning architectures against traditional methods. Furthermore, the paper delves into the interpretability of deep learning models, shedding light on their decision-making process in the context of malware detection. the findings of this research not only demonstrate the efficacy of deep learning in bolstering cybersecurity measures but also contribute valuable insights into the evolving landscape of malware defense strategies. © 2023 IEEE.

关键词： Recurrent neural networks

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Research on the application of an OPT Model Integrating Meta-learning and Prompt learning for Few-Shot Event Extraction 4

Research on the Application of an OPT Model Integrating Meta...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Li, Xianda Azhati, Baheti Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798400708831

Event extraction plays a pivotal role in natural language processing (NLP), especially in few-shot learning environments where research is increasingly growing. this paper proposes an OPT model that integrates Model-Agnostic Meta-learning (MAML) and prompt learning to enhance the performance of few-shot event extraction. Our method was tested on the ACE2005 dataset and compared with existing models. the results demonstrate the effectiveness of our approach in improving few-shot event extraction. © 2023 ACM.

关键词： Extraction

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4th international conference on Design Tools and Methods in Industrial Engineering, ADM 2024

4th International Conference on Design Tools and Methods in ...

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4th international conference on Design Tools and Methods in Industrial Engineering, ADM 2024

ISBN: (纸本)9783031765964

the proceedings contain 157 papers. the special focus in this conference is on Design Tools and Methods in Industrial Engineering. the topics include: the Simultaneous Effect of FFF Build Orientation on Tensile Strength, Flatness and Roughness;reasons Behind Selecting a Manufacturing Process: A Semi-structured Interview Study;2D Frequency-Based Topological Optimization: machine learning to Aid Tuning Simulation Parameters;A Short Overview of Optimization Methods for BCC, FCC and Octet Lattice Structure;advancements in Design of Personalized Bone Scaffolds and Fabrication through 3D Printing;lightweighting of a Kart Connecting Rod in Additive Manufacturing;Design of a Local Heating System for Cost-Effective Fused Filament Fabrication of PEEK;a Multi-Criteria Analysis Method in Algorithm-Driven Design;a Design Method for Graded Density Components by Polylactic Acid Foaming via Material Extrusion;A New BCC Lattice Modelling Approach through a Neural Network Optimisation;design and Assessment of Functionally Graded Lattices;A Strategy Towards In Situ Monitoring and Quality Assessment of 3D Components Manufactured by FDM: Preliminary Features;Integration Between AI and TRIZ for Technical Innovation;engineering Design and Prototyping Methodology in Complex Environments: the In-Bore Welding Tool;machine learning for Costing Sheet Metals;application of a Simplified Value Analysis Methodology (VAM-2P) and CAD/CAE Tools to Agro-Industrial Product Redesign: Harvesting Trolley Case Study;Preliminary Concept Design of an Innovative Under-Actuated Gripper for the Remote Handling Operations in the RFX-Mod2 Fusion machine;Preliminary Design of a General Control Architecture for the DTT Remote Handling Facility;study and application of a New Index to Support Design for Disassembly;digital Product Passports: A Framework for Developing Adaptable and Compliant Data Models.

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Comparative Analysis of machine learning Algorithms on Prediction of Alzheimer from MRI 6

Comparative Analysis of Machine Learning Algorithms on Predi...

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Indhumathi, G. Palanivelan, M. Senthilkumar, K. Sairam, V.A. Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India Central Polytechnic Collegel Tharamani Department of Electronics and Communication Engineering Tamilnadu Chennai600113 India Symbiosis Institute of Technology Depaqrtment of Ai and Ml Maharashtra Pune412115 India

ISBN: (纸本)9798350394702

Alzheimer's Disease (AD) is a well-recognized cause of dementia among the elderly population, affecting an increasing number of individuals. the origins and progression mechanisms of AD remain intricate and not yet fully comprehended. Magnetic Resonance Imaging (MRI) is a neuroimaging technique that enables in-depth and precise investigations of the disease, providing a valuable tool for both diagnosis and early detection of AD. However, handling extensive sets of clinical images poses a significant challenge, prompting researchers to explore machine learning methods. machine learning encompasses a range of computer-assisted techniques that adapt their outcomes toward predefined objectives. this study incorporates various machine learning techniques and conducts a comparative analysis to determine the most effective approach for Alzheimer's Disease detection. Additionally, a web application has been developed, enabling physicians to predict a patient's condition automatically. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Auto-Tagging User Traits in the Context of Smartphones 4th

Auto-Tagging User Traits in the Context of Smartphones

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4th international conference on Design, Operation and Evaluation of Mobile Communications (MOBILE) Held as Part of the 25th international conference on Human-computer Interaction (HCII)

作者： Sistla, Srija Jayakumar, Nikitha Smrithi, K. Akhil, G. B. S. Pujari, Nitin, V Pes Univ Banashankari Stage 3 Bangalore 560085 India

ISBN: (纸本)9783031359200;9783031359217

In the past few decades research has been done on the prediction of personality traits of users from social media to enhance users experience while using social media applications. Most often we tend to be more upfront with our smartphones and personalise it to suit our daily moods and emotions. these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviour. Questionnaire based personality tests pose certain challenges as test takers may not be able to accurately answer the questions due to various personal or professional reasons on a survey. Employing a methodology based in the context of smartphone application usage data is an attempt to increase the accuracy in identifying the user's personality traits as this data will be more closely related to the user's changing behavioural pattern over a set of time periods.

关键词： Human Behaviour machine learning App Usage Usage Stats Big Five Personalities Personality Traits Recursive Feature Elimination Decision Trees Random Forest Gradient Boosting

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Exploring Wordle- Based on Improved machine learning Models 4

Exploring Wordle- Based on Improved Machine Learning Models

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4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Li, Boyu Zuo, Kuangji Xiao, Hongde Miami College of Henan University Kaifeng City China Mathematics and Statistics College of Henan University Kaifeng City China

ISBN: (纸本)9798350326444

this paper presents three models for predicting, classifying, and evaluating game data, which is collected from Wordle's Twitter account. Model I is a predictive model based on an improved LSTM algorithm that uses Sliding window to improve accuracy. By using short-term predictions, the training set is updated, reducing long-term uncertainty. Model II is a predictive distribution model based on PLS and MLP algorithms, PLS algorithm is used to predict the linear part, while the MLP algorithm compensates for the deficiency in the nonlinear part. Model III is a K-means classification model that uses the elbow rule to determine the K-value of 2, which divides games into two categories: Hard Type and Easy Type. the difficulty assessment value determines which category a game falls into. the results from this study demonstrate the effectiveness and accuracy of the proposed models in analyzing complex and variable data and provide insights into the correlation between different game attributes and their impact on game difficulty. © 2023 IEEE.

关键词： Long short-term memory

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

4th International Conference on Smart IoT Systems: Innovatio...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

ISBN: (纸本)9789819751990

the proceedings contain 40 papers. the special focus in this conference is on Smart IoT Systems: Innovations and Computing. the topics include: Systematic Literature Review on Role of AI in IoT-Based Healthcare Solutions;a Hybrid machine learning Model for Lung Disease Prediction;analyzing Student Feedback with Natural Language Processing for Enhancing Student Perception;enabling Technology for Smart and Intelligent Transportation: Analyse the Uses and Impact of Blockchain Technology and Artificial Intelligent on Trucking Industry for Better Results;Detecting Parkinson Disease Using Various AI Enabled Methods: Recent Advances, Requirements, and Open Challenges;synergistic Fusion of Steganography and Cryptography in Medical Imaging: A Comparative Analysis to Enhance Image Security;detecting Inattentive and Aggressive Driver Behavior Using Deep learning: Recent Advances, Challenges with Performance Evaluation;Improved Brain Tumor Classification Accuracy Using CNN Architecture with Efficient Net on Magnetic Resonance Imaging;noise Prediction: A Case Study of Gjilan City;economic Crimes in Current Era: World and Indian Context;analyzing the Citation Networks Using Community Detection Approaches: A Review;A Hybrid Correlation Feature Selection Based Support Vector machine (CFS-SVM) for Effective Software Defect Prediction;mitigating Risks in Vehicle Fires: An Integrated Approach for Early Detection and Real-Time Alert System;multimodal Data Fusion: Combining Big Data and Deep learning for Enhanced Predictive Models;Analyzing Artificial Intelligence Based Intrusion Detection System in Detecting DDoS Attack;preserving Our Heritage: Buildings Deep learning Solutions for Monitoring Cultural Heritage Structures Using Automated Crack Detection;Insurance_4_YOU: MCDM Equipped User-Friendly Intelligent System for Personalized Life Insurance Recommendations;apriori Analysis of Deep learning Models on a Multi-class Data Set.

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the application of Deep learning in Image Deblurring 24

The Application of Deep Learning in Image Deblurring

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4th international conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Jiang, Chengchao Hu, Lei Jia, Guotao Xi'an Precision Machinery Research Institute Kunming Branch Kunming Yunnan China

ISBN: (纸本)9798400710247

Images are an important medium for people to obtain information. In the process of acquiring images, it is inevitable that images will become blurred due to various reasons. Image restoration is a technical method that uses algorithms to enhance image quality and is an important research field in computer vision. It is widely used in industrial, agricultural, and medical image processing. In recent years, with the rapid development of deep learning technology, an increasing number of image restoration algorithms based on deep neural networks have been proposed. this paper mainly introduces image restoration technology based on deep neural networks from the perspective of image deblurring. the paper first elaborates on the overall classification of image restoration algorithms, then discusses the causes of image blurring and commonly used traditional algorithms, deep learning algorithms, and models. Finally, the paper summarizes the advantages and disadvantages of various image restoration methods and prospects for the future development of image restoration technology. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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