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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411-420 订阅

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Hyacinth macaw: a project-based learning program to develop talents in software engineering for Artificial Intelligence 23

Hyacinth macaw: a project-based learning program to develop ...

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37th Brazilian Symposium on software engineering, SBES 2023, held in conjunction with the Brazilian conference on software: Theory and Practice, CBSoft 2023

作者： Fontão, Awdren Matsubara, Edson Mongelli, Henrique Medeiros, Marcio Lourenço, Carlos Martins, Henrique Cortez, Igor Borges, Maria Faculty of Computing / Software Engineering Lab Federal University of Mato Grosso Do Sul Brazil PDtec SP Brazil B3 SP Brazil Blk SP Brazil Neoway SP Brazil

ISBN: (纸本)9798400707872

software engineering for Artificial Intelligence (SE4A) uses SE principles to design and maintain AI systems, requiring analytical thinking for software complexity, while AI demands mathematical knowledge and algorithm adjustment. The IEEE Curriculum Guidelines for Undergraduate Degree Programs in software engineering states that extracurricular elements impact students' preparation. This study focuses on the first module of a project-based learning talent development program involving undergraduate students, two expert professors (in AI and SE), and mentors from sponsoring companies. An exploratory case study with 39 students from four courses was conducted, challenging them to deliver an MVP in machine learning within 1.5 months. Results showed high agreement (87.5%) in applying learned skills to future projects, recognizing SE's benefits (96.9%) in AI, and acknowledging the connection between SE and AI (78.1%). Participants applied relevant knowledge in ML performance, data analysis, and software architecture for AI. We share strategies used by students to enhance developer experience. © 2023 ACM.

关键词： Students

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ADDITIVELY MANUFACTURED NITIHF SHAPE MEMORY ALLOY TRANSFORMATION TEMPERATURE EVALUATION BY RADIAL BASIS FUNCTION AND PERCEPTRON NEURAL NETWORKS 18

ADDITIVELY MANUFACTURED NITIHF SHAPE MEMORY ALLOY TRANSFORMA...

引用

ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Abedi, Hossein Abdollahzadeh, Mohammadjavad Almotari, Abdalmageed Ali, Majed Mohajerani, Shiva Elahinia, Mohammad Qattawi, Ala Univ Toledo 2801 W Bancroft St Toledo OH 43606 USA

ISBN: (纸本)9780791887233

Employing Laser Powder Bed Fusion (LPBF) method to manufacture NiTiHf Shape Memory Alloy (SMA) is becoming more common. The major design property for NiTiHf is the transformation temperatures (TTs) which control the activation threshold of the SMA material and enable it to create the shape change due to a microstructure phase transformation. Given the high number of fabrication factors, machine learning (ML) approaches provide a promising approach to the design of SMA to control the TTs. The main obstacle to using ML methods is the need for an established correlation between fabrication features and material properties. The presented work develops an ML approach to enable the prediction of the TTs for additively manufacturing NiTiHf. The work uses all available experimental data on additively and conventionally manufactured NiTiHf. Selected fabrication features included in the ML models consider the elemental compositions of NiTiHf, laser power, laser speed, hatch spacing, and almost all the processing steps historically used to manufacture, or heat treat the NiTiHf for SMA. Multiple models of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Neural Networks (NN) are developed to predict the TTs of LPBF-manufactured NiTiHf. The models successfully predict the TTs for various NiTiHf fabrication conditions.

关键词： Data-driven Material Design Laser Powder Bed Fusion NiTiHf Shape Memory Alloys Transformation Temperatures machine learning Additive Manufacturing

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A QoS and Load Balancing Predictive Model Based on LSTM and Random Forest Regression in SDN: A Rest API Approach 4th

A QoS and Load Balancing Predictive Model Based on LSTM and ...

引用

4th international conference on Data and Information in Online Environments

作者： Ansari, Muhammad Salman Zhang, JianXun Shaban Tianjin Univ Technol & Educ Tianjin Peoples R China Univ Elect Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9783031807121;9783031807138

Fog Computing, software Define Networking, RESTful API and machine learning are new technologies in the area of ICT as well as in Networking. Fog computing brought high performance computing at the edge of network and software Defined networking. As we intended to head towards QoS and Load balancing in SDN. In this work we cover an initial segment which is machine leaning model implementation on a network traffic information to predict future outcomes for traffic flow. Meanwhile, in comprehensive research we intended to reduce network congestion, jitter, QoS and load balancing by learning past traffic flow information. As well as, we aim to improve maximum utilization of link-bandwidth. Through RESTful API of SDN, we can embed machine learning based prediction model to the network server which can modify the network policies by learning from past experiences. In this paper, we proposed a QoS and load balancing framework. A Server and SDN based application for network monitoring, management and controlling the policies over network gateway for better performance in regard of traffic flow. Experiment result shows that implementation of machine learning over network traffic flow information immensely important for new emerging technologies. The evaluation results of machine learning model we implemented in this work depicts that model performs well. Meanwhile the model improves by increasing with the number of epochs.

关键词： machine learning in Networking SDN RESTful API QoS and Load balancing

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HYDROSTATIC STABILITY EXPLORATION ON FLOATING STRUCTURES USING machine learning 37

HYDROSTATIC STABILITY EXPLORATION ON FLOATING STRUCTURES USI...

引用

37th international conference on Coastal engineering, ICCE 2022

作者： ElDarwich, Hamid S. Pawitan, Krisna Adi Mansouri, Iman Garlock, Maria M. Princeton University United States

ISBN: (纸本)9780989661164

A hydrostatic stability analysis is an important first step in designing floating structures. Most of the currently available commercial software is limited to hydrostatic stability curves. Current research tries to address this limitation, by developing a framework which couples numerical hydrostatic stability analysis based on potential energy minimization, with a machine learning (ML) model based on genetic programming (GP). In this way, potential energy functions are efficiently obtained. The resulting analytical formulations offer a wider understanding of the hydrostatic stability of floating structures. © 2023 American Society of Civil Engineers (ASCE). All rights reserved.

关键词： Stability

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Vulnerability Detection and Monitoring Using LLM 9

Vulnerability Detection and Monitoring Using LLM

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9th IEEE international Women in engineering (WIE) conference on Electrical and Computer engineering, WIECON-ECE 2023

作者： Akuthota, Vishwanath Kasula, Raghunandan Sumona, Sabiha Tasnim Mohiuddin, Masud Reza, Md Tanzim Rahman, Md Mizanur Drpinnacle Hyderabad India Green University of Bangladesh Department of Computer Science and Engineering Kanchon Purbachal American City1460 Bangladesh BRAC University Department of Computer Science and Engineering 66 Mohakhali Dhaka1212 Bangladesh

ISBN: (纸本)9798350319651

Large Language Models (LLMs) have evolved as a cornerstone for intricate code evaluations in the modern realm of artificial intelligence and machine learning. The prioritizing of rigorous security requirements is a crucial requirement for the business in the dynamic and ever-changing world of software development. The current study has used the capabilities of the GPT-3.5- Turbo model to conduct a detailed assessment of various code snippets to find any vulnerabilities. The main objective of the experiment was to introduce continuous monitoring technologies to enhance software security and release control. To obtain reliable results, we used a classification report and a confusion matrix. Out of these validation methods we choose accuracy as an important metric for this validation because in this experiment we need our model to predict the vulnerabilities that are present in the 2740 test cases and we would need our model to focus more on true positives(TP). The ideal goal of this experiment was to predict any kind of vulnerability from the real-world data. Out of all test cases, we were able to have an accuracy of 0.77. This demonstrates the approach's potential efficacy in discovering vulnerabilities. Nonetheless, the study found certain parts that require improvement, emphasizing the importance of continual refinement in the model's methodology to ensure more thorough security assessments. This study lays the groundwork for future research into the use of powerful machine learning models in the assessment of software vulnerabilities. The findings not only highlight the effectiveness of the existing approach but also offer light on prospective future research directions, paving the way for the next generation of models and evaluation techniques. © 2023 IEEE.

关键词： machine learning

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Prediction of Distributed Denial of Service Attacks in SDN using machine learning Techniques 3

Prediction of Distributed Denial of Service Attacks in SDN u...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Sekar, R. Raja Jenny, Ardhala Mounika Sreshta, Dubba Vikas, M. Ajay, Dasari Badri Nageshwar Ganesh, Mankena Kalasalingam Academy of Research and Education Department of Computer Science Amd Engineering Virudhnagar Krishnankovil India

ISBN: (纸本)9798350338607

A network architecture known as "software-defined networking"(SDN) enables the design and creation of hardware elements remotely. Due to their permanent connections, you can modify on the go. Even if SDN remains fantastic approach, Internet-threatening DDoS occurrences may exploit it. DDoS occurrences may be stopped with the use of machine learning techniques. When numerous systems work together to assault a server, it is called a DDoS attack. A control layer software manages the devices of the infrastructure layer and enables SDN by linking the application layer to the infrastructure layer. [1] The methods for identifying malicious traffic that we discuss in this work include decision trees, Cat Boost, and Extra trees. The outcomes of our tests demonstrate that Decision Tree, Cat Boost, and Extra Tree are capable of determining if the attack is safe or not. © 2023 IEEE.

关键词： Decision trees

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machine learning - Based Prediction of Compressive Strength Analysis

Machine Learning - Based Prediction of Compressive Strength ...

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2023 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Gowri, R. Vasanthi, P. Swathi Rekha, R. Vijaya, B. Karthikeyan, S. Harinishrii, V.S. Chennai India Chennai Institute of Technology Dept. of Civil Engineering Chennai India Dr. M.G.R.Educational and Research Institute Civil Engineering Chennai India Chennai Institute of Technology Civil Engineering Chennai India Sri Sairam Institute of Technology Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350348910

This study's main goal is to assess concrete's compressive strength using the soft computing technique in a way that uses less time, money, and material resources. Using machine learning (ML) in the creation of concrete mixes is expanding swiftly because of its revolutionary potential and ability to address difficult challenges. To forecast the water absorption and compressive strength of concrete at 28 days of age, a comparative study was carried out utilizing the 'JUPITER' software environment and the ML method Artificial Neural Network (ANN). The dataset was produced in a controlled laboratory setting. It is demonstrated that the suggested ANN model predicts the compressive strength of concrete ratio with a high degree of accuracy using the coefficient of determination (R2) and root-mean-square error (RMSE) for validation. © 2023 IEEE.

关键词： Water absorption

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Research on indoor robot navigation algorithm based on deep reinforcement learning 2

Research on indoor robot navigation algorithm based on deep ...

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2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Lei, Yaohua Cheng, Yunli Tan, Xueqin Tan, Yanxian Software Engineering Institute of Guangzhou Guangdong Guangzhou510990 China

ISBN: (纸本)9798350331417

Highly intelligent robot is the trend in the new round of development, and it is also the ultimate goal of robot navigation technology *** ability to learn is an important embodiment of robot *** navigation is the basic function of indoor mobile robots[1]and it is the premise and key technology to realize the autonomy and intelligence of *** the continuous development of robot-related technologies and the continuous expansion of application scope, the tasks faced by robots are more challenging, and the work scenes are increasingly complex and diversified, which puts forward new requirements and challenges for navigation technology. Indoor robot navigation systems need not only to maintain accuracy and stability in fixed scenes, but also to be able to handle dynamic changes in the environment, and to take into account safety and the impact on human comfort in an environment that coexists with *** paper takes the indoor mobile robot platform as the research object[2], introduces the machine learning algorithm based on neural network into the navigation task[3], utilizes the existing research achievements in the field of deep learning to build a robot navigation system based on different learning modes, and endows the robot with intelligent perception ability and navigation strategy learning ability[4],. © 2023 IEEE.

关键词： Imitation learning Indoor robot navigation Navigation algorithms reinforcement learning

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Review on Dysarthric Speech Severity Level Classification Frameworks 5

Review on Dysarthric Speech Severity Level Classification Fr...

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Suresh, Manju Thomas, Joshua College of Engineering Trivandrum Thiruvanathapuram India Mar Baselios College of Engineering and Technology Thiruvananthapuram India

ISBN: (纸本)9798350334128

Dysarthria is a motor speech disorder that results from malfunctions in the mechanisms and systems which regulate the movements required for speech production. It is challenging to organise, programme, manage, coordinate, and carry out speech productions due to these neurological limitations. Assessing the degree of dysarthria severity can provide information about the patient's progress, assist pathologists in planning a treatment plan, and make automated voice recognition software for dysarthria more useful. This paper provides a thorough analysis of fresh and cutting-edge machine learning and deep learning methods and algorithms for dysarthric speech severity level classification. Depending on the degree of the impairment, various acoustic features and feature selection techniques are considered for the precise classification of dysarthric speech. This study is focused on various deep learning methodologies as well as the comparison of outcomes with various machine learning algorithms. An overview of the data sets and evaluation metrics frequently utilised in the classification of dysarthric severity levels is also included in this review. Finally, an audio-video cross model approach is reviewed for the comparison of severity level classification based on different features using different classifiers. © 2023 IEEE.

关键词： Classification (of information)

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A Survey on the Utilization of Artificial Intelligence and machine learning in the Field of Network Functions Virtualization and software Defined Networking 3

A Survey on the Utilization of Artificial Intelligence and M...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Piakaray, Divya Somashekhara Reddy, D. Goswami, Subhashish Rajasekaran, SureshBabu Sharma, Kuldeep Alfurhood, Badria Sulaiman Arka Jain University Department of Computer Science Jharkhand Jamshedpur India Department of Computer Science and Engineering Bangalore India Dev Bhoomi Uttarakhand University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar Phagwara India Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia

ISBN: (纸本)9798350399264

Artificial based Intelligence (AI) as well as machine learning (ML) are regularly acquiring a tremendous attention from the scholarly world in taking care of exceptionally complex issues in a few domains. In this research document, we put forward a short study of the fundamental usage areas of simulated intelligence/ML in S-D-N and NFV oriented networks. We order the fundamental progressions nearby in various classifications in view of the way those techniques applied in track and distinguish the relating AI procedures used. Furthermore, recognize and talk about the fundamental difficulties furthermore, future headings nearby. We firmly state that AI as well as ML can assume a fundamental role in giving a mA-N-N er for self based designed, self based adaptive and independent organizations. Nonetheless, the examination is restricted due the distinguished difficulties around here. © 2023 IEEE.

关键词： machine learning

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