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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3307 条记录，以下是411-420 订阅

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. the system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Federated learning

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Health Risks and Disease Transmission in Undocumented Immigrants in the U.S Using predictive ML 6

Health Risks and Disease Transmission in Undocumented Immigr...

引用

6th international conference on Intelligent Computing in Data Sciences, ICDS 2024

作者： Abir, Shake Ibna Shoha, Shaharina Al Shiam, Sarder Abdulla Uddin, Md Milon Islam Mamun, Md Atikul Shamsul Arefeen, S.M. Western Kentucky University Department of Mathematics Kentucky United States St Francis College USA Department of Management New York United States The University of Texas at Tyler Dept. of Electrical Engineering Texas United States Stephen F. Austin State University Department of Chemistry and Biochemistry Texas United States University of Massachusetts Boston Master of Science in Business Analytics Boston United States

ISBN: (纸本)9798350351200

the main issue is undocumented migration, it presents a significant concern to public health due to the potential spread of infectious illnesses and overall health risks. Data was gathered between January 2018 and December 2022. the present study employs contemporary technology methodologies, including big data analytics and machine learning, to better understand health risks and the dissemination patterns of prominent diseases among undocumented immigrants. the main infectious diseases, included Tuberculosis, hepatitis, and chronic disorders. Interpretation: the study findings indicate a greater likelihood of specific contagious diseases among undocumented immigrants. the applied machine learning models successfully obtained diseases health risk information. According to our experimental analysis, the Convolutional Neural Network and K-Nearest Neighbors model achieved the highest performance with 90% Precision, 90% Recall, and 80% F1-Score, respectively, compared to other machine learning techniques-Logistic Regression (80% Precision, 80% Recall, and a 70% F1-Score), Decision Tree (70% Precision, 60% Recall, 70% F1-Score), and Naïve Bayes (50% Precision, 80% Recall, and 60% F1-Score). the study will be helpful in promoting scrutiny in analyzing public health records and understanding of diseases associated with undocumented immigrants in the U.S. © 2024 IEEE.

关键词： Electronic health record

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A Concise Account for Challenges of Machine Learning in Seakeeping 6

A Concise Account for Challenges of Machine Learning in Seak...

引用

6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Majidiyan, Hamed Enshaei, Hossein Howe, Damon Department of Nautical Studies Australian Maritime College University of Tasmania Launceston7248 Australia

the rise of Artificial Intelligence (AI) and machine learning (ML) in recent years has encouraged practitioners to explore new solutions for longstanding and complex issues, resulting in significant advancements across various fields. In the realm of seakeeping and ocean engineering, researchers have begun testing ML-based data-driven models, leading to a surge in scientific articles aimed at boosting autonomy in control and furthering ship sailing. However, despite the growing interest in ML, the inherent stochastic characteristics of environmental forces have often been overlooked. Many studies have been limited to specific scenarios and single fidelity data, applying various networks developed in software engineering to seakeeping without addressing the field's intrinsic challenges. As a result, the outlook for deploying and integrating ML-based models into real-world onboard systems remains obscure. this paper aims to address this gap by presenting a systematic framework to better evaluate the applicability of ML networks in maritime studies, particularly in real-world applications. We discuss environmental factors and associated challenges in respective ML models, systematically outlining a framework that illustrates feasible availabilities and solutions. Additionally, we introduce a set of criteria that allows for the comparison of ML networks for current purposes and benchmarking their feasibility concerning real-world physics and intended applications. the outcomes of this study can pave the road for the future generation of autonomous ships and marine vessels. © 2024 the Authors. Published by Elsevier B.V.

关键词： Ocean engineering

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE international conference on Data engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. this research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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DriveRP: RAG and Prompt engineering Embodied Parallel Driving in Cyber-Physical-Social Spaces 4

DriveRP: RAG and Prompt Engineering Embodied Parallel Drivin...

引用

4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Huang, Jun Ma, Hao Zhang, Tengchao Lin, Fei Ma, Siji Wang, Xiao Wang, Fei-Yue Macau University of Science and Technology Faculty of Innovation Engineering China Anhui University School of Artificial Intelligence Hefei China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

In recent years, numerous technological advancements in Artificial Generative Intelligences (AGIs) have demonstrated significant potential to transform the intelligence acquisition mechanisms in connected autonomous vehicles (CAVs). Integrating technologies like ChatGPT into CAVs can enhance human-machine interactions. However, the emergence of such new traffic entities may introduce unforeseen hallucinations and complex risks that surpass our current understanding. To address these challenges, Retrieval-Augmented Generation (RAG) and prompt engineering technologies are being explored to enhance the reliability and safety of autonomous driving systems. RAG retrieves relevant contextual information, such as driving experiences and real-time road network status, from external databases to ensure that foundation models have access to accurate and timely data for informed decision-making. Prompt engineering optimizes the performance of large language models in autonomous driving systems by designing and refining prompts that guide the models' responses, thereby improving their relevance and accuracy in various driving scenarios. Together, these technologies enhance the robustness and trustworthiness of autonomous driving systems. this paper proposes DriveRP, a framework that integrates RAG and prompt engineering within the Descriptive-predictive-Prescriptive Intelligence framework of Parallel Driving theory. DriveRP aims to enhance the safety and interpretability of autonomous vehicle trajectory planning, decision-making, and motion control, ultimately achieving the "6S"goals. Grounded in Digital Twins and Metaverse-embodied parallel driving theory, DriveRP provides the infrastructure and foundational intelligence for parallel driving with Multi-modal Large Lange models(MLLMs). Additionally, the paper discusses future trends and potential research directions, focusing on the "6S"goals of parallel driving: Smart, Safe, Secure, Sensitive, Sustainable, and Serviceable. © 2

关键词： Intelligent systems

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Artificial Intelligence Based Employee Attrition Analysis and Prediction 6

Artificial Intelligence Based Employee Attrition Analysis an...

引用

6th international conference on Computing and Informatics, ICCI 2024

作者： Ozakca, Nazli Sila Bulus, Ali Cetin, Aydin Havelsan Inc.Command Control and Defence technologies Ankara Turkey Computer Engineering Department Gazi University Faculty of Technology Ankara Turkey

ISBN: (纸本)9798350373875

the use of classification models to predict the probability of an employee leaving the job can greatly improve the ability of human resources to intervene in a timely manner and rectify the situation to prevent attrition. After the so-called "the Great Resignation", which took place after the pandemic period, studies on attrition have increased in order to regain the lost workforce and increase the loyalty of existing employees. In Turkey, however, studies in this field remain insufficient. the aim of this study is to predict the possible loss of employees, to take relevant measures and to reduce the financial losses of companies. the study was made available as SaaS (software as a Service) based in order to facilitate accessibility to the target audience. For the analysis phase, the columns in the data set were analyzed with Pandas and Scikit-Learn library. Exploratory Data Analysis was performed by visualizing the analyzed data with Plotly and Seaborn libraries. Various insights were obtained with these inferences. the data was subjected to pre-processing stages such as cleaning, missing data completion, scaling, feature selection, data set balancing, dimension reduction, etc. to make it useful. Logistic Regression, KNN, SVM, Desicion Tree, Random Forest, ADABoost and Naive Bayes were used to train the model. In order to enrich the data set and increase the efficiency of the model, artificial intelligence-based synthetic data generation (Data Augmentation) was performed. In case of missing columns in the trained model, KNN-Data Imputation, one of the missing data completion methods, was used. In the optimization process of the model, hyperparameter optimizations were performed to achieve maximum efficiency with improvements. 5-fold cross-validation prevented the model from over-learning. Performance was analyzed on the basis of Accuracy, Recall, Precision and F1-score metrics and the success criterion was determined as F1-Score. the model was presented as a web se

关键词： Efficiency

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Machine Learning based URL Analysis for Phishing Detection 6

Machine Learning based URL Analysis for Phishing Detection

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Jha, Rashmi Kunwar, Gaurav Computer Science and Engineering Management Education and Research Institute Janakpuri New Delhi India Computer Science and Engineering Grd Imt Dehradun India

ISBN: (纸本)9798350346961

Attacks that take place online most frequently take the form of phishing scams. Attackers attempt to gather user data without the consent of any such user through emails, URLs, and any other link that sends a viewer to the a dubious page where a consumer is persuaded to start taking specific actions that can successfully complete an attack. An attacker has the opportunity to gather vital information about the victim during these attacks, which they can use to presume the victim's identity & carry out tasks that only the victim should have been able to carry out, such as making purchases, sending messages to the other people, and simply attempting to access the victim's info. Many studies have been done to discuss potential defenses against these assaults. this study employs three algorithms for machine learning to ascertain whether such a web page is phishing. In the experiment, software that analyzes web page URLs to distinguish between legitimate & phishing websites is used to try to prevent attacks using these models which have been trained to utilize URL-based features. the accuracy, recall, and F1 Score of the random forest classifier's performance from the observations were 97.5%, 99.1%, and 97.3%, respectively. the proposed model is quick and effective because, unlike earlier studies, it only relies on the URL and doesn't conduct analysis using any other sources.. © 2023 IEEE.

关键词： Websites

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A predictive Study of ARIMA Model Based on Multi-Bayesian Estimation Method Fused Data on Building Materials Environment 23

A Predictive Study of ARIMA Model Based on Multi-Bayesian Es...

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6th international conference on Big Data Technologies, ICBDT 2023

作者： Xu, Yan Wu, Gaoyun Song, Wei School of Software & Internet of Things Engineering Jiangxi University of Finance and Economics China School of Information Management Dalarna University Sweden

ISBN: (纸本)9798400707667

Due to the uncertainty and inconsistency of measurement data from multiple sensors in the same space, a multi-sensor data fusion algorithm is used to fuse the measurement data of multiple nodes. We propose a multi-Bayesian estimation method for fusing multi-sensor data, and combine Bayesian estimation with ARIMA model to predict the ambient temperature of bamboo and wood building materials. It can utilize the redundancy of data to reduce this uncertainty and improve the reliability of subsequent predictions. © 2023 ACM.

关键词： Uncertainty analysis

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Nonlinear Dynamics based Trajectory Tracking Robust Control of Unmanned Ground Vehicle 6

Nonlinear Dynamics based Trajectory Tracking Robust Control ...

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6th CAA international conference on Vehicular Control and Intelligence, CVCI 2022

作者： Song, Jiarui Tao, Gang Zang, Zheng Li, Derun Fu, Xingjie Gong, Jianwei School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

this paper investigates the issue of unmanned ground vehicles (UGVs) trajectory tracking. A model predictive control and nonlinear dynamics-based robust control (MPC-NDRC) framework is suggested for UGV to improve trajectory tracking performance. the MPC-NDRC framework is divided into two phases. Building a predictive model controller is the first step in preventing the issue of poor real-time performance brought on by online computing complex models. the creation of a robust nonlinear dynamics-based controller is the second step in ensuring the performance of trajectory tracking and controller model accuracy. Additionally, by designing the system's poles with a stability margin, the system's robust stability is ensured. the Lyapunov theorem establishes the sufficient condition for closed-loop system stability. the MATLAB-Carsim platform's simulation results show the proposed MPC-NDRC framework considerably improves trajectory tracking performance. © 2022 IEEE.

关键词： Model predictive control

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Pruning Networks Using Filters Similarity Stability 23

Pruning Networks Using Filters Similarity Stability

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6th international conference on software engineering and Information Management, ICSIM 2023

作者： Qu, Haicheng Zhang, Xuecong College of Software Liaoning Technical University Huludao China

ISBN: (纸本)9781450398237

Current filter pruning methods rely too much on pretrained weights and have many super parameters, resulting in obvious performance degradation and too long parameters adjustment time. In our research, we found that the cosine similarity distribution between filters can achieve stable in a few epochs during training. therefore, a cluster pruning method named ECP(Early Cluster Pruning) based on the cosine similarity between filters in the early stage of training is proposed to compress the deep neural networks. First, in the early stage of training, the filters were clustered with a gradually increasing threshold, and then the reserved filters were selected randomly in each cluster. the pruned models could be obtained with only a few super parameters and a single training progress, leading to an obvious reduction in algorithmic complexity and large savings in training time. the experimental results on CIFAR-10 and CIFAR-100 datasets show that ECP method outperforms recent pruning methods in terms of model accuracy maintenance, training time, and model compression rate. © 2023 ACM.

关键词： Deep neural networks

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