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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

共 11649 条记录，以下是621-630 订阅

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Comparison Feature Selection Particle Swarm optimization (PSO) with Evalutionary optimization Against Naive Bayes Algorithm on the Big Five Analysis Personality on Twitter Social Media 4

Comparison Feature Selection Particle Swarm Optimization (PS...

引用

4th International conference on Electronic and Electrical Engineering and intelligent System, ICE3IS 2024

作者： Ompusunggu, Nunut Rejeki Firmansyah, Gerry Widodo, Agung Mulyo Tjahjono, Budi Universitas Esa Unggul Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350378368

the Big Five Personality is one of the theories of trait theory that has a very important role about personality and this model can represent the core of personality theory to describe, interpret, and predict human behavior. this study explores Naive Bayes' classification optimization to predict the personalities of the Big Five individuals based on their activity on Twitter using evolutionary optimization techniques. Social media platforms, particularly Twitter, provide rich datasets to analyze user behavior and personality. this study uses Evolutionary Algorithm to improve the accuracy of Naive Bayes' classification through feature selection optimization, thereby improving the model's performance in classifying personality traits. the research methodology involves preprocessing Twitter data, feature extraction, and the application of an optimized Naive Bayes model. the results show the effectiveness of evolutionary optimization in improving classification accuracy, providing insight into personality predictions from social media activities. the best test of all the experiments that have been conducted in this study is the Naïve Bayes Algorithm Model combined with Evalutionary feature selection with an accuracy difference of 11.00% from the Naïve Bayes Algorithm test and the difference from the test with the Naïve Bayes Algorithm Model combined with PSO feature selection of 10.25%. thus, Evalutionary is the best feature selection to help improve the performance of the Naïve Bayes Algorithm in the Big Five Personality Classification on Twitter Social Media. © 2024 IEEE.

关键词： Tweets

来源：评论

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FlexFlow- the Nutrients and Recommendation Application Using Machine learning 4

FlexFlow- The Nutrients and Recommendation Application Using...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Elavarasi, D. Kavitha, R. Jayabarathan, Jayson K. Aanjankumar, S. Mount Zion College of Engineering and Technology Department of Computer Science and Engineering Pudukkottai India Velammal College of Engineering Madurai India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331529635

Flexflow is an advanced web application designed to provide personalized dietary recommendations tailored to individual needs and health goals. By considering factors such as age, sex, height, weight, activity level, and dietary preferences, Flexflow generates customized meal plans and offers valuable insights into nutritional composition, relevant recipes, and exercise recommendations. this comprehensive approach empowers users to make informed dietary choices and achieve their health and wellness objectives. By utilizing data-driven insights and personalized recommendations, Flexflow has the potential to significantly improve dietary habits and promote healthier lifestyles across diverse populations. © 2024 IEEE.

关键词： Food ingredients

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Hybrid Deep learning Models with Adversarial Defense Mechanisms for Secure Beamforming Prediction 4

Hybrid Deep Learning Models with Adversarial Defense Mechani...

引用

4th intelligent Cybersecurity conference, ICSC 2024

作者： Abasi, Ammar Kamal Aloqaily, Moayad Guizani, Mohsen Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350354775

Millimeter-wave (mmWave) communication in 6G networks presents unique challenges, such as susceptibility to blockages and rapidly changing environments. this paper introduces a truly new hybrid deep learning (DL) architecture, which synergistically combines convolutional neural networks (CNNs) and vision transformers (ViTs), to enhance beamforming prediction. CNNs efficiently process spatial data crucial for beamforming, while ViTs model long-range dependencies vital in dynamic 6G scenarios. this integrated approach not only aims to boost prediction accuracy but also incorporates tailored adversarial defenses against attacks like the Fast Gradient Sign Method (FGSM) to fortify security. the key contributions are: 1) Developing an innovative hybrid CNN-ViT architecture tailored for 6 G beamforming prediction;2) Integrating novel adversarial defense mechanisms to enhance security;3) Extensive simulations and real-world validation demonstrating significant performance and security improvements over existing methods. the proposed solution paves the way for robust and secure 6G network deployments by overcoming mmWave propagation complexities while mitigating adversarial threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Root Cause Analysis Framework for intelligent Computing Center 4

Root Cause Analysis Framework for Intelligent Computing Cent...

引用

4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Lin, Junpeng Gao, Yifei Ye, Jiantao Miao, Dandan Zhang, Yinzhu Lin, Ke Wang, Yiyue Zhang, Chen Tsinghua University Department of Industrial Engineering Beijing China Huawei Technologies Songshan Lake Research Center Dongguan China Huawei Technologies Shanghai Research Center Shanghai China

ISBN: (纸本)9798331531225

the rapid development of AI technology has greatly increased the demand for computational power, leading many companies to deploy large-scale intelligent computing centers. However, the larger cluster sizes result in more frequent failures, making root cause analysis more challenging. To address these issues, we propose ASeqRCA, a novel framework for root cause analysis in intelligent computing center. We simplify the complex network structure of intelligent computing clusters and model the relationships between alarms in sequences by frequent-pattern mining and causal learning techniques. With an alarm causal graph, we apply depth-first search to identify potential alarm propagation paths. We illustrate the effectiveness of the proposed method through a real-world AI model training failure case. © 2024 IEEE.

关键词： Complex networks

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MBTI Personality Type Prediction Using BERT-LSTM and Deep learning on Social Media Posts 4

MBTI Personality Type Prediction Using BERT-LSTM and Deep Le...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Rani, V. Shobha Reddy, Chittireddy Akhil Ramu, V. Babu, A. Ramesh Reddy, Vallem Randheer Shruthi, B. School of Cs&ai Sr University Warangal India Iit Kharagpur Dept of Mathematics West Bengal India Mrec Dept of CSE-DS Secundrabad India Dept of Cs Hyderabad India Mrec for Women Dept of Cse Hyderabad India Kits Dept of It Warangal India

ISBN: (纸本)9798331529635

It is well known that everyone has a particular 'personality' meaning a set of cognitive, affective, and behavioral characteristics. the current study uses text classification based on deep learning models i.e. BERT and LSTM, to predict user's personality traits from their Facebook posts. With the Myers-Briggs type indicator (MBTI) as a baseline, the current research investigates the relationship between user-generated content and personality traits. the results reveal that such an architectural ensemble of BERT contextual embeddings with LSTM's advantages over sequence data indeed results in higher accuracy over personality trait predictions than strategies like Naive Bayes and SVM machine learning algorithms. It was found that personality forecasting systems may benefit from the implementation of advanced NLP models, thus increasing the accuracy and usability of the systems, especially when targeting social media users. © 2024 IEEE.

关键词： Prediction models

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Enhanced Ransomware Detection using a Hybrid Quantum-Classical Convolutional Neural Network with Boosted Sooty Tern optimization 4

Enhanced Ransomware Detection using a Hybrid Quantum-Classic...

引用

4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Chaithanya, B.N. Patil, Renuka R. Gitam School of Technology Gitam University Department of Computer Science and Engineering Nagadenahalli Bengaluru India

ISBN: (纸本)9798331523923

Ransomware attacks threaten organizations by encrypting files or locking systems and keeping them inaccessible unless a ransom is paid. Early detection of ransomware attacks helps organizations avoid financial losses, theft of data, and harm to their reputations. this early detection will also reduce the risks related to malicious activities. Detection by signature-based or heuristic approaches tends to be traditional and fails when evolvement in ransomware attacks is considered. thus, a need for innovative techniques of deep learning and optimization algorithms can be observed. the method proposed in this study focuses on developing a novel multi-stage framework for detecting ransomware attacks. Firstly, correlation coefficient-based min-max normalization (CCMMN) for data preprocessing is used since it makes the data elements more uniform and makes the detection system more efficient. Feature selection is then carried out and optimized using the hybrid Osprey optimization algorithm with an enhancing sparrow search algorithm (OOA-ESSA) to arrive at the most relevant feature for classification. Last, this paper applies a hybrid quantum-classical convolutional neural network with boosted sooty tern optimization (HQCCNN-BSTO) in ransomware detection. the classic model is replaced by a quantum and hybrid classical neural network that may be complemented by an optimization algorithm to increase the speed and efficiency of the detection. the method that has been proposed here offers a flexible and efficient approach to the problems associated with the detection of ransomware threats in the practical environment. thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. this exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Convolutional neural networks

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Implementing Resource-Aware Scheduling Algorithm for Improving Cost optimization in Cloud Computing 4

Implementing Resource-Aware Scheduling Algorithm for Improvi...

引用

4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Reddy, Kiran Jagannadha Udayaraju, Pamula Pamaiahgari, Venkata Pradeep Reddy Kumar, V. Dilip Walmart BentonvilleAR United States Srm University Department of Cse AP India Wendys International Llc OH United States SRKR Engineering College Department of CSE India

ISBN: (纸本)9798331523923

An uncountable number of computational resources are shared for various applications using a transformative technology called Cloud Computing. It is an emerging technology that can offer scalable and on-demand resources. However, cost optimization and resource allocation are critical issues due to the increasing number of cloud user and their request. It can be solved by optimising resource allocation by reducing the task/user queue. It is possible only if the requested resource is free;otherwise, it must schedule the same or relevant resource. this paper demonstrates a Resource-Aware Scheduling (RAS) algorithm to increase the speed of resource allocation by mapping the user request with the resource availability. the proposed algorithm examines the log information of the resources, workload behaviour, resource availability, price, task efficiency, and reduced wastage and allocates the resources dynamically. To do that, it maps user information with resource information and schedules based on availability and priority. the simulation-based experiment is carried out in a private cloud space and demonstrates the resource allocation, utilization, response time, and cost. It is also compared with the traditional scheduling algorithm to evaluate the performance of the RAS. the evaluation found that the RAS model is more suitable for cloud resource allocation. © 2025 IEEE.

关键词： Cloud Computing Cost optimization Resource Allocation Resource-Aware Scheduling Scheduling

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Research on intelligent Sensitive Data Recognition and Annotation Technology Based on Long Text 4

Research on Intelligent Sensitive Data Recognition and Annot...

引用

4th International conference on Electronics, Circuits and Information Engineering, ECIE 2024

作者： Fu, Hua Meng, Liang Wei, Sisi Nong, Chunkai Tan, Yu Guangxi Power Grid Co. Ltd. Nanning Power Supply Bureau Nanning China Information Communication Branch of Guangxi Power Grid Company Nanning China

ISBN: (纸本)9798350388312

With the rapid development of information technology, the widespread dissemination of long text data in cyberspace has brought challenges to personal privacy and information security. this study aims to develop an intelligent sensitive data recognition and annotation technology for long text content. By integrating natural language processing (NLP) technology with machine learning algorithms, we propose an efficient recognition framework that can accurately extract and annotate sensitive information from long texts. the research results indicate that the framework performs well in processing long text data, effectively improving the accuracy of sensitive data recognition, and providing solid technical support for privacy protection and data security. © 2024 IEEE.

关键词： Data assimilation

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A ChatGPT-enabled intelligent Assistive Technology for Homecare to Support Activities of Daily Living for People with Mild Dementia 24

A ChatGPT-enabled Intelligent Assistive Technology for Homec...

引用

4th International conference on Human-Machine Interaction (ICHMI)

作者： Chen, Xiaowei Fan, Guoliang Roberts, Emily Howell, Steven, Jr. Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Oklahoma State Univ Dept Design & Merchandising Stillwater OK 74078 USA

ISBN: (纸本)9798400716812

People with dementia (PwD) face significant cognitive challenges that hinder their ability to perform daily tasks, referred to as activities of daily living (ADLs), and are particularly impeded when it comes to the more intricate instrumental ADLs (IADLs). Lacking sufficient support, individuals with dementia are at a greater risk of losing their independence and becoming more vulnerable. A range of intelligent assistive technologies (IATs) have been proposed to help individuals with mild dementia perform certain ADLs on their own. However, most current IATs offer limited support for IADLs to PwD, primarily due to the technologies inability to comprehend the unique communication needs of PwD and a lack of user-specific customization. this paper presents a new proposed homecare assistive tool for people with mild dementia, called CATcare (Cognitive Assistive Technology Care). Our CATcare system, utilizing ChatGPT, offers a comprehensive understanding of PwD. Easily operated and configured via smartphone or smart glasses, it allows caregivers to tailor it to the unique IADL requirements of each care recipient. Additionally, it offers step-by-step guidance, prompts, and timely feedback to help PwD complete their desired tasks. Our research leverages the capabilities of transfer learning from AI models in indoor localization, object detection, and Natural Language Processing (NLP) and shows the potential and promise to develop a customizable and personalizable CATcare tool. this CATcare system is intended to improve the quality of life of PwD and reduce the burden of their caregivers.

关键词： ChatGPT transfer learning people with mild dementia intelligent assistive technologies audio/visual prompting indoor localization instrumental activities of daily living

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Understanding Model Extraction Games 4

Understanding Model Extraction Games

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IEEE 4th International conference on Trust, Privacy and Security in intelligent Systems, and Applications (IEEE TPS-ISA)

作者： Xian, Xun Hong, Mingyi Ding, Jie Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665474085

the privacy of machine learning models has become a significant concern in many emerging Machine-learning-as-a-Service applications, where prediction services based on welltrained models are offered to users via the pay-per-query scheme. However, the lack of a defense mechanism can impose a high risk on the privacy of the server's model since an adversary could efficiently steal the model by querying only a few `good' data points. the game between a server's defense and an adversary's attack inevitably leads to an arms race dilemma, as commonly seen in Adversarial Machine learning. To study the fundamental tradeoffs between model utility from a benign user's view and privacy from an adversary's view, we develop new metrics to quantify such tradeoffs, analyze their theoretical properties, and develop an optimization problem to understand the optimal adversarial attack and defense strategies. the developed concepts and theory match the empirical findings on the `equilibrium' between privacy and utility. In terms of optimization, the key ingredient that enables our results is a unified representation of the attack-defense problem as a min-max bi-level problem. the developed results are demonstrated by examples and empirical experiments.

关键词： Adversarial learning Bi level optimization Machine learning security Minimax optimization

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