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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3232 条记录，以下是381-390 订阅

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An Automated Strabismus Classification Using machine learning Algorithm for Binocular Vision Management System 8

An Automated Strabismus Classification Using Machine Learnin...

引用

8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Isyraf Rohismadi, Muhammad Amirul Mat Raffei, Anis Farihan Akmar Zulkifli, Nor Saradatul Ithnin, Mohd. Hafidz Othman, Shah Farez Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Pahang26600 Malaysia Kulliyah of Medicine International Islamic University Malaysia Kuantan Pahang25200 Malaysia

ISBN: (纸本)9798350310931

Binocular vision is a type of vision that allows an individual to perceive depth and distance using both eyes to create a single image of their environment. However, there is an illness called strabismus, where it is difficult for some people to focus on seeing things clearly at a time. There are many diagnoses that need to be done for doctors to diagnose whether patients suffer from strabismus or not. Besides, a new practitioner could lead to misdiagnosis due to lack of professional experience and knowledge. To overcome these limitations, a machine learning algorithm, which is a case-based reasoning, is developed to automate the strabismus classification. The results showed that the case-based reasoning algorithm provides 91.8% accuracy, 89.29% precision, 92.59% recall and 90.91% F1-Score. This shows that using the case-based reasoning algorithm can give better performance in classifying the class. © 2023 IEEE.

关键词： Case based reasoning

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A Priori Lane Selection Strategy for Reinforcement learning of Dynamic Expressway Tolling

A Priori Lane Selection Strategy for Reinforcement Learning ...

引用

2023 international conference on Pattern Recognition, machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Zhang, Xi Wang, Wei Chen, Jing Yunnan University School of Software Kunming China Yunnan University School of Information Science and Engineering Kunming China

ISBN: (纸本)9798350346596

Dynamic tolling of toll roads is a way to dynamically adjust the toll rates according to the changing road traffic conditions in order to alleviate traffic congestion and improve commuting efficiency. Aiming at the dynamic toll collection problem of Chinese expressway, we design a reinforcement learning simulation environment for China's expressway network and propose a reinforcement learning dynamic toll model based on a priori lane selection strategy that adapts to the characteristics of the network and travelers' travel habits. Experiments show that the reinforcement learning-based dynamic tolling can increase the total revenue by more than 10% compared with the fixed- rate tolling scheme and keep the congestion rate at a low level. In addition, the ablation experiments demonstrate that the priori knowledge-based lane selection model can better weigh the "total revenue", "system throughput"and "total system travel time"of the optimized road network under the joint optimization objective © 2023 IEEE.

关键词： Reinforcement learning

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Implementation of a Physically Unclonable Function on an FPGA

Implementation of a Physically Unclonable Function on an FPG...

引用

2023 IEEE international conference on Smart Information Systems and Technologies, SIST 2023

作者： Baideldinov, Marat Usserbaev, Muratbek Mukanova, Zhanna L.N. Gumilyov Eurasian National University Department of 'Computer and Software Engineering' Astana Kazakhstan S.Seifullin Kazakh Agro Technical University Department 'Technological Machines and Equipment' Astana Kazakhstan Turan University Department of 'Computer and Software Engineering' Almaty Kazakhstan

ISBN: (纸本)9798350335040

This paper shows the implementation of a physically unclonable function (PUF) of the arbiter type and its modifications, the generation of random numbers and unique identifiers, and a statistical analysis of the generated sequences of random numbers. Within the framework of this work, experiments were carried out on different SoC, SoM crystals and within the same crystal with different placement on it, experiments with PUF modeling based on machine learning were simulated. The results and conclusions provide recommendations for the optimal and safe use of PUF. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Sentiment analysis based brand recommendation system: A review

Sentiment analysis based brand recommendation system: A revi...

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international conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Rastogi, Chaitanya Singh, Darshika Dwivedi, Ashutosh Chaudhary, Anshika Aggarwal, Sahil Kumar Jain, Ruchi Information Technology ABES Engineering College Ghaziabad India

ISBN: (纸本)9781032669663

A brand recommendation system with sentiment analysis is a software tool that uses natural language processing techniques to analyze consumer opinions and sentiments towards various brands. The system analyses data from multiple sources such as social media, online reviews, and survey responses to understand consumer preferences and make recommendations accordingly. Brand recommendation systems using sentiment analysis can be a valuable tool for businesses looking to improve customer experience and drive brand loyalty. This paper reviews various methods supporting sentiment analysis and their effectiveness in each condition. The review includes the research paper on sentiment analysis from 2014 to 2021 in different categories for prediction. The parameters chosen in the paper for reviewing different algorithms used in machine learning are response time, complexity, type of data on which methods are applied, a different area where methods can be applied and obstacles and opportunities where other machine learning methods work well with pre- and post-conditions. © 2024 The Author(s).

关键词： Sentiment analysis

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Advancements in Dental Diagnostics: YOLOv3-Powered machine learning for Automated Teeth Problem Detection 24

Advancements in Dental Diagnostics: YOLOv3-Powered Machine L...

引用

24th international Arab conference on Information Technology, ACIT 2023

作者： Odeh, Suhail Samara, Anas Zreineh, Nicola Obeid, Seif Ibrieghieth, Maen Obaid, Mahmoud Bethlehem University Software Engineering Dept. Palestine Arab American University Computer System Engineering Jenin Palestine

ISBN: (纸本)9798350384307

This paper presents a rigorous investigation into the application of state-of-the-art machine learning techniques for the automated detection of dental issues, utilizing the YOLOv3 Algorithm, a cutting-edge one-stage object detection method. The study encompasses the meticulous annotation and utilization of a carefully curated dataset featuring 126 panoramic X-ray images, illustrating a diverse range of dental conditions. Among these conditions, the primary emphasis is placed on the precise detection of six specific dental problems. Employing advanced computer vision methodologies, the model demonstrates exceptional accuracy in the identification and precise localization of these targeted dental issues. The implications of these findings are profound for the field of dental healthcare, as the automated detection of dental problems holds the potential to significantly enhance the diagnostic and treatment planning capabilities of dental professionals. The results presented in this research represent a notable stride in the ongoing evolution of machine learning and underscore its capacity to revolutionize the landscape of dental diagnostics, ultimately contributing to the advancement of oral health management and patient care. © 2023 IEEE.

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A software Credibility Framework with a Decision Making Component for Patient Portals 3

A Software Credibility Framework with a Decision Making Comp...

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3rd IEEE international conference on software engineering and Artificial Intelligence, SEAI 2023

作者： Rawlins, Marcus Aalon Yu, Xuejun Beijing University of Technology School of Software Beijing China

ISBN: (纸本)9798350337952

In today's digital era, when software applications are omnipresent across multiple areas, software credibility is critical. Based on Yu et al.'s model, this paper extends this model and proposes a software credibility framework with accumulation and decision-making components for patient portals. A basic patient portal prototype was used to test the framework, and three test cases were generated to demonstrate the various phases of the decision-making component. The results show that the framework effectively monitored malicious user activity and can be further integrated into real-time applications such as banking apps. The suggested framework has the potential to significantly increase software credibility and dependability, and more work can be done to extend the framework by employing Artificial Intelligence and machine learning to automatically learn and assign user behavioral states over time. © 2023 IEEE.

关键词： Decision making

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Code Analysis to Prioritize and Fix Bugs Using Top-K Pruning Approach with help of machine learning 5

Code Analysis to Prioritize and Fix Bugs Using Top-K Pruning...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Rastogi, Vansham Pandey, Ayushi Galgotias University School Of Computer Science & Engineering Uttar Pradesh Greater Noida203201 India

ISBN: (纸本)9798350330861

The Empirical Study of Bug fixing is a critical and time-consuming task in software development. With the increasing complexity of software systems, identifying and resolving bugs has become even more challenging. To address this issue, automated bug detection and correction tools have been developed to assist developers in detecting and fixing bugs quickly and efficiently. In this paper, we present Bug Fixer, a tool for automated bug detection and correction. Bug Fixer utilizes machine learning algorithms to identify and prioritize bugs, and it also provides developers with automated fixes for the detected bugs. We evaluate the effectiveness of Bug Fixer by conducting experiments on open-source software projects. Our results show that Bug Fixer is effective in detecting and fixing bugs, and it significantly reduces the time and effort required for bug fixing compared to manual approaches. We also discuss the limitations and future directions of Bug Fixer and conclude that automated bug detection and correction tools like Bug Fixer have the potential to improve software quality and accelerate the software development process. © 2023 IEEE.

关键词： Open source software

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Research on UBI Claims Risk Probability Models Based on machine learning 4

Research on UBI Claims Risk Probability Models Based on Mach...

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4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Li, Dongyu Yu, Hao Zhu, Xu China Automotive Technology & Research Center Co. Ltd Tianjin China

ISBN: (纸本)9798350329490

In recent years, with the development of big data and the popularization of the Internet of vehicles, driving behaviors can be effectively recorded and analyzed, and applied in various automotive post-market fields, including insurance pricing, model risk classification, and so on. With the reform of commercial vehicle rates, more and more insurance companies have the power to set their price. Compared with the traditional influencing factors, driving behavior has a more significant impact on the risk probability. Therefore, it is necessary to adjust the premium based on driving behavior. Based on the collected data of 100 driving behaviors and accident records, this paper uses the traditional Logistic Regression model and three popular machine learning algorithms: KNN algorithm, Random Forest algorithm, and XGBoost model to compare their prediction results on risk probability. By comparison, it is found that XGBoost model has higher prediction accuracy. Therefore, this algorithm is used to rank the importance of the influencing factors to the risk probability, to provide a basis for insurance companies to formulate subsequent premiums, and also, it can improve the driving behavior of drivers, and reduce the traffic accident rate. © 2023 IEEE.

关键词： Insurance

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Enhancing Intrusion Detection Systems Accuracy Using machine learning 10

Enhancing Intrusion Detection Systems Accuracy Using Machine...

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10th international conference on software Defined Systems, SDS 2023

作者： Daoud, Mohammad Sh. Shawar, Bayan Abu Alyafeai, Mohammad Essam, Amro Ghassan, Mohammed Alzaabi, Nahyan College of Engineering Al Ain University Abu Dhabi United Arab Emirates Aixplain Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350382235

Following the digital revolution, massive amounts of data have been produced over time through a variety of networks. The networks have made data analysis exceedingly challenging by identifying attacks using the appropriate approaches. Although intrusion detection systems (IDSs) protect resources from threats, they still struggle with increasing detection accuracy, cutting down on false alarm rates, and finding unidentified threats. Making machines intelligent is the aim of artificial intelligence, and knowledge acquisition is made possible via machine learning. Making a computer self-learner is the goal of machine learning (ML), a sub-field of artificial intelligence. This capacity for self-learning will be useful in tackling many challenging issues. Utilizing a machine learning intrusion detection system can increase its effectiveness and ability to recognize new attack patterns through self-learning or knowledge acquisition. IDS are the first line of defense because they gather data or knowledge from a network and analyze it to identify components that are breaking network and computer security laws. In this paper, some machine learning algorithms are going to be implemented onto an IDS to measure their detection accuracy. The K-Fold cross validation method is going to be performed in order to increase the detection rate. As well as run the algorithms multiple times in order to find out which features hold more importance when compared with other features. The machine learning algorithms K-Nearest Neighbor (KNN), Decision tree, and Random Forest are going to be implemented. RF with 100 trees outperformed the other algorithms with an accuracy of 92.65%. © 2023 IEEE.

关键词： machine learning

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AutoML from software engineering Perspective: Landscapes and Challenges 20

AutoML from Software Engineering Perspective: Landscapes and...

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IEEE/ACM 20th international conference on Mining software Repositories (MSR)

作者： Wang, Chao Chen, Zhenpeng Zhou, Minghui Peking Univ Sch Comp Sci Beijing Peoples R China Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China UCL London England

ISBN: (纸本)9798350311846

machine learning (ML) has been widely adopted in modern software, but the manual configuration of ML (e.g., hyper-parameter configuration) poses a significant challenge to software developers. Therefore, automated ML (AutoML), which seeks the optimal configuration of ML automatically, has received increasing attention from the software engineering community. However, to date, there is no comprehensive understanding of how AutoML is used by developers and what challenges developers encounter in using AutoML for software development. To fill this knowledge gap, we conduct the first study on understanding the use and challenges of AutoML from software developers' perspective. We collect and analyze 1,554 AutoML downstream repositories, 769 AutoML-related Stack Overflow questions, and 1,437 relevant GitHub issues. The results suggest the increasing popularity of AutoML in a wide range of topics, but also the lack of relevant expertise. We manually identify specific challenges faced by developers for AutoML-enabled software. Based on the results, we derive a series of implications for AutoML framework selection, framework development, and research.

关键词： AutoML software engineering application challenge

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