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检索条件"任意字段=International Conference on Machine Learning and Computing"

共 65243 条记录，以下是4781-4790 订阅

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Classification of Social Anxiety Disorder Using Explainable machine learning and Pearson's Correlation Technique 8

Classification of Social Anxiety Disorder Using Explainable ...

引用

8th IEEE international conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Bathula, Srivarsha Chadaga, Krishnaraj Prabhu, Srikanth Sampathila, Niranjana Manipal Institute of Technology Manipal Academy of Higher Education Manipal Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350350593

Social Anxiety Disorder (SAD) is a widespread mental health issue marked by significant fear or discomfort during social interactions. It can greatly affect a person's life and overall happiness, causing problems like emotional distress, low self-esteem and depression. Thorough examination by a mental health expert is often required to diagnose SAD. The diagnostic and statistical manual of mental disorders' particular criteria are used to diagnose SAD that includes clinical interviews, self-report questionnaires, and a thorough evaluation of the individual's symptoms like severe anxiety while engaging or conversing with strangers. In this work, we employ explainable artificial intelligence (XAI) and machine learning techniques to identify SAD in individuals. Critical attributes were identified using Pearson's correlation technique. Random Forest yields optimal outcomes with 88% accuracy, 93 % precision, 83 % recall, 84 % F1-score, and 94% Area Under Curve (AUC). Furthermore, XAI methods such as Shapley Additive Values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) have been applied to improve the models' accuracy, comprehensibility, and precision. Automated SAD diagnosis helps in early detection and increased accessibility that allows for timely intervention, treatment, and facilites access to social anxiety testing and screening for an individual. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Thermal Comfort in Buildings with machine learning-Based Overheating Prediction

Enhancing Thermal Comfort in Buildings with Machine Learning...

引用

2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Rajalekshmi, Mithun Vijayakumar Hashemi, Arman Sharif, Mhd Saeed Intelligent Technologies Research Group University of East London London United Kingdom Dept. Of Architecture and Visual Arts University of East London London United Kingdom

ISBN: (纸本)9798331533137

The goal of this project is to improve the application of machine learning techniques in the summertime prediction of thermal comfort in residential structures (for both present and future weather situations). Using DesignBuilder’s integrated simulation engine and simulated data, the research creates strong prediction models with Random Forest and XGBoost algorithms. Essential factors like building orientation, window-to-floor ratios, U-values, and operating temperatures were examined using exploratory data analysis, feature engineering, and thorough data preparation. Mean Absolute Error (MAE) and R-squared values were applied for the accurate and effective validation of the models. The findings demonstrate significant potential for early-stage decision making on building designs, for reducing risk of overheating and opening the door to more sustainable and comfortable living spaces. Future research endeavors aim to enlarge the dataset, explore different machine learning modeling techniques, and enhance the models’ capability to predict and mitigate overheating in different building kinds and climatic conditions. ©2024 IEEE.

关键词： Thermal comfort

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machine learning Based Hardware Accelerated Cobot Grasping in the Food Industry 11

Machine Learning Based Hardware Accelerated Cobot Grasping i...

引用

11th international conference on Electrical, Electronic and computing Engineering, IcETRAN 2024

作者： Ivačko, Nikola Ćirić, Ivan Ćojbašić, Žarko Milošević, Maša Jevtić, Dušan University of Nis Faculty of Mechanical Engineering Nis18000 Serbia Fazi doo Belgrade11000 Serbia

ISBN: (纸本)9798350386998

In food industry automation, the integration of machine learning with robotic systems can introduce improved efficiency and precision in various tasks such as fruit and vegetable handling and manipulation. This paper introduces an innovative approach to robotic grasping, specifically customized for the challenge of identifying and handling apples and oranges within a dynamic and unstructured environment. Utilizing the YOLO (You Only Look Once) object detection algorithm, the system consists of a camera mounted on the robot's end effector, enabling real-time identification and localization of fruits and vegetables. Implemented on the NVIDIA Jetson platform, this solution presents the combination of machine learning techniques with hardware acceleration, ensuring optimal performance in real-time object recognition and manipulation tasks. Our experimental setup tests the robustness of our object recognition and localization approach under varying lighting and orientation scenarios and evaluates the system's overall performance in a real-world, cluttered environment typical of the food industry. The findings of this study underscore the potential of integrating ML-powered vision systems with hardware-accelerated robotics for food handling and manipulation applications. By demonstrating high levels of accuracy in recognition, classification and grasping, our research contributes to the broader field of robotic automation, offering insights into the scalability and adaptability of such systems across different sectors of the food industry. © 2024 IEEE.

关键词： Fruits

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Evaluation and Optimization of machine learning Algorithms in Personalized Marketing 3

Evaluation and Optimization of Machine Learning Algorithms i...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Guo, Chunxiang Jiao, Pengfei School Of Economics And Management Shaanxi Fashion Engineering University Shaanxi Xi'an China

ISBN: (纸本)9798350318609

With the development of society and the continuous updating of economic models, the importance of marketing for business operations is becoming increasingly high. However, due to the traditional marketing model adopting a popular strategy, personalized recommendations are insufficient. In recent years, machine learning algorithms have been widely applied and developed rapidly in various industries. Therefore, this article studied the effectiveness evaluation and optimization of machine learning algorithms in personalized marketing, and discussed the business recommendation marketing of banks as the research point. The study was conducted using the dataset provided by the Kaggle data competition website. Firstly, customer information was analyzed and divided into 12 explanatory variables and 1 response variable. Next, the Apache Nifi tool was used to collect information, ensuring the security of customer information. Then, the Informatica Data Quality tool was used to clean the information data and organize it into features that can be utilized by the algorithm. The chi square test was used to test the correlation of features, determine the value of a as 0.05, and select the appropriate feature component to input into the model. Finally, the GBDT (Gradient Boosting Decision Trees) algorithm was used to analyze features, determine the actual needs of customers, and provide personalized recommendations to customers. The research results showed that the model had a 94% precision in judging the personal wishes of customers, and had strong anti-interference ability and good robustness. This study can accurately infer customers' intentions based on customer information, providing a new approach for exploring personalized marketing. © 2024 IEEE.

关键词： Sales

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A Comprehensive Investigation Into the Implementation of machine learning Solutions for Network Traffic Classification

A Comprehensive Investigation Into the Implementation of Mac...

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2023 international conference on Advanced computing and Communication Technologies, ICACCTech 2023

作者： Joon, Deepanshi Pundir, Meena Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350380880

machine learning-based network traffic categorization has become a crucial field of study because of the rising intricacy of Internet services and the expanding usage of en-cryption. A thorough examination of the application of machine learning techniques for automated network traffic classification and analysis is given by this systematic study. A comprehensive review of the literature was done using many large scientific databases in order to find pertinent research that used machine learning methods for traffic categorization. 52 papers were chosen for final assessment out of over 700 initial results, based on relevance, rigour, and creativity. Important models, characteristics, datasets, algorithms, and limits related to traffic categorization are summarised, along with significant trends. Analysis reveals that larger traffic datasets, more feature sets, and a greater use of deep learning structures have all contributed to enhanced model resilience and accuracy. Nevertheless, problems still exist with real-time deployment, encrypted traffic analysis, and model generalisation across networks. Intelligent traffic categorization systems have the potential to improve network security, provide better quality of service, and provide insights into usage patterns, but additional advancements are necessary for real-world applicability. © 2023 IEEE.

关键词： Network security

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Designing Incentive Mechanisms for Fair Participation in Federated learning 25

Designing Incentive Mechanisms for Fair Participation in Fed...

引用

25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xu, Han Nanda, Priyadarsi Liang, Jie University of Technology Sydney Faculty of Engineering and It Sydney Australia

ISBN: (纸本)9798350330014

Federated learning offers a collaborative method for training machine learning models using distributed data sources. Given its reliance on diverse contributions from multiple participants, equitable representation and rewards are paramount to its success. Upholding fairness is crucial to encourage active participation in this collaborative process. This paper presents a comprehensive analysis of existing mechanisms designed to incentivise fair engagement in federated learning, structured around a taxonomy aligned with the evolving dynamics of the federated learning sources. The study offers insights into cultivating environments that prioritise fairness and broad participation while suggesting avenues for future research. © 2023 IEEE.

关键词： learning systems

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machine learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)

Machine Learning Algorithms for Stroke Risk Prediction Lever...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Ugbomeh, Ogochukwu Yiye, Versse Ibeke, Ebuka Ezenkwu, Chinedu Pascal Sharma, Vandana Alkhayyat, Ahmed Robert Gordon University School of Creative and Cultural Business Aberdeen United Kingdom Robert Gordon University School of Computing Engrg.and Tech Aberdeen United Kingdom Christ University Computer Science Department India Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350378092

Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I'm Five (ELI5). With the interpretations, healthcare providers can gain insight into patients' stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system. © 2024 IEEE.

关键词： Adversarial machine learning

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6th international conference on Emerging Technologies in computing, iCETiC 2023

6th International Conference on Emerging Technologies in Com...

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6th international conference on Emerging Technologies in computing, iCETiC 2023

ISBN: (纸本)9783031502149

The proceedings contain 15 papers. The special focus in this conference is on Emerging Technologies in computing. The topics include: On the Design and Performance Evaluation of Android Based Alarming Applications;investigation of Air Effluence Using IoT and machine learning;exploring the Emerging Technologies Within the Blockchain Landscape;socialEcho: A Social Networking Platform with Community Guidelines Violation Pre-check;green-IoT Based Automated Field Maintenance System;chaotic Chimp Based African Vulture Optimization Algorithm with Stability Tests for Feature Selection Algorithms;event-Based Data Pipelines in Recommender Systems: The Data Engineering Perspective;pre-planning for Plastic Surgery Using machine learning: A Proof of Concept;academic Integrity in the Face of Generative Language Models;docBot: A System for Disease Detection and Specialized Doctor Recommendation Using Patient’s Speech of Symptoms;Digitalisation Transformation in High Schools: Analysis of the COVID-19 Pandemic’s Accelerating Impact;Cube Attacks on Round-Reduced Grain-128AEAD;a Literature Review of Various Analysis Methods and Classification techniques of Malware.

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Leveraging Hybrid machine learning Technique For Effective House Rental Price Forecasting 11

Leveraging Hybrid Machine Learning Technique For Effective H...

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11th IEEE international conference on Advances in computing and Communications, ICACC 2024

作者： Komalavalli, D. Anbalagan, Naveenkumar Madhumitha, M. Najma, N. Kaviyanbu, K. Mathiyarasi, P. Sona College of Technology Department of Information Technology Tamil Nadu Salem India Sona College of Technology Department of Information Technology Salem India

ISBN: (纸本)9798350379136

These days, figuring out the house's sale price is crucial because both the land and the house's prices rise annually. Therefore, the next generation needs a simple approach for forecasting future rents for houses. Accurate and data-driven strategies are crucial for estimating house rental prices in the rapidly shifting real estate market. This study investigates the application of machine learning to accurate rental price forecasting. Conventional methods of rental estimation tend to rely on intuition and knowledge of the marketplace in the area, but they are incapable of taking into consideration the complex network of variables which influence rental rates. This study explores how rental prices are influenced by factors including the age of the property, whether it is furnished, the flat area, the size of the house etc. It also shows how machine learning models may be used to find hidden connections and get precise rental price predictions. The dearth of highly accurate models currently in use, the need to reduce overpricing, adjust to changing conditions in the market, maximize the return on investments, support data-driven decision-making, advance market transparency, and create a simple to use interface are among the reasons behind this research. The main goal is to create a reliable and accurate model for predicting the price of a rental home so that tenants and property owners can benefit from data-driven insights. © 2024 IEEE.

关键词： Regression analysis

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Mental Health and Suicide Risk among College Students: A Detailed Exploration of Contributing Factors 15

Mental Health and Suicide Risk among College Students: A Det...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Meena, R. Gunasundari, B. Omana, J. Sripriya, N. Yamini, G. Revathi, K.P. Easwari Engineering College Department of AI&DS Chennai India Prathyusha Engineering College Department of CSE Chennai India VIT University School of Computer Science and Engineering Chennai India Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of English Chennai India

ISBN: (纸本)9798350370249

This study aims to identify critical predictors of suicide and inform targeted interventions using data from 27,014 college students. It examines a comprehensive range of mental health indicators, including psychiatric symptoms, suicidal intentions, impulsivity, sleep disturbances, internet addiction, aggression, self-injury behaviors, and eating problems, alongside demographic and socioeconomic variables. Analytical techniques, including exploratory data analysis, cross-tabulation, and correlation analysis, uncover the complex interplay between these factors and suicide risk. The study evaluates several machine learning algorithms. The weighted Random Forest uses feature weighting based on point-biserial correlation coefficients to enhance prediction accuracy and prioritize relevant features. The findings reveal significant associations between higher scores in mental health indicators and increased suicidal behavior, emphasizing the need for comprehensive mental health support systems. By identifying key risk factors through machine learning models, the study provides actionable insights for developing targeted interventions aimed at reducing suicide risk among college students. © 2024 IEEE.

关键词： Feature weighting Fuzzy evaluation method machine learning models Point-biserial correlation coefficients

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