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Research on Food Image Recognition of Deep learning Algorithms 4

Research on Food Image Recognition of Deep Learning Algorith...

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4th international conference on Computers, Information Processing and Advanced Education, CIPAE 2023

作者： Luo, Lihua Guangdong University of Science and Technology College of Software Engineering Dongguan523000 China

ISBN: (纸本)9798350342710

In recent years, food image recognition has been a research field that has received much attention. Diet is an essential part of people's daily life. Through food image recognition and classification, dietary evaluation is conducted to achieve the goal of healthy eating. Manual features and classification methods in traditional target detection algorithms can no longer meet the requirements of detection accuracy and speed, so it is necessary to introduce depth learning-based techniques into food image recognition. Deep learning is a complex machine learning algorithm that allows computers to simulate the human brain's thinking, judge new problems through data sets (human experience), and improve themselves to obtain relatively accurate models, just like humans. Among them, convolutional neural networks have developed most rapidly. They are applied to image recognition with the expectation of being able to automate recognition through machine learning, thereby reducing the workload of image recognition and providing more objective analysis results. Deep learning has achieved great success in intelligent image processing, image classification, and recognition and has excellent development potential and social value. This article implements automatic recognition of food images based on the recognition algorithm of the YOLOv5 convolutional neural network structure, which can detect food images with high detection efficiency. It explores the combination of deep learning to identify food images, providing a reference for people to eat healthily, especially for obese people to control calorie intake, and providing theoretical support for food image recognition. © 2023 IEEE.

关键词： Image recognition

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Multi Model Approach to Evaluate and Enhance Student - Teacher Interactions 5

Multi Model Approach to Evaluate and Enhance Student - Teach...

引用

5th international conference on Advancements in Computing, ICAC 2023

作者： Daksith, Nisal Wanaguru, Sathsara Kolonne, Uresha Dolawatta, Amaya Abeywardhana, Lakmini Kasthurirathna, Dharshana Sri Lanka Institute of Inoformation Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Inoformation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Advancements in artificial intelligence (AI) and machine learning (ML) have enabled the development of tools to address issues in Virtual Classrooms. This research focuses on utilizing AI and ML to monitor student behavior in virtual classrooms by analyzing facial expressions, background noise, and keystroke patterns. An AI based approach is proposed to analyze real-time webcam data, detecting patterns of inattention, and notifying teachers accordingly. The application also monitors background noise levels, identifying non-conducive learning environments, and analyzes keystroke patterns to detect unrelated activities. The research draws from academic literature to evaluate the effectiveness of the application while considering ethical implications such as potential bias and student privacy protection. Overall, this study contributes to the existing literature on AI and ML-based applications in education, demonstrating how these technologies can revolutionize student behavior monitoring, leading to enhanced outcomes for virtual classroom learning. © 2023 IEEE.

关键词： machine learning

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A Step to Achieve Personalized Human Centric Privacy Policy Summary 18

A Step to Achieve Personalized Human Centric Privacy Policy ...

引用

18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Simon, Ivan Haggag, Sherif Haggag, Hussein Univ Adelaide Fac Sci Engn & Technol Adelaide SA Australia Umea Univ Dept Comp Sci Umea Sweden

ISBN: (纸本)9789897586477

Online users continuously come across privacy policies for the service they use. Due to the complexity and verbosity of policies, majority of the users skip the tedious task of reading the policy and accept it. Without reading and evaluating the document users risk giving up all kinds of rights to their personal data and for the most part, are unaware of the data sharing and handling process. Efforts have been made to address the complex and lengthy structure of privacy policies by creating a standardized machine-readable format of privacy policies for the web browsers to process it automatically, a repository of crowdsourced summarized versions of some privacy policies, or by using natural language processing to summarize the policies. PirvacyInterpreter is one unique tool that acknowledges human-centric factors while summarising the policy. Thus, it generates a personalised summary of the privacy policy for the user providing relevant information to appease their privacy concerns. This paper presents the conceptualization of PrivacyInterpreter and implements a proof-of-concept model using configured RoBERTa(base) model to classify a privacy policy and produce a summary based on privacy aspects that reflect users' privacy concerns.

关键词： Privacy Policy Text Summarization Human-Centric machine learning

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A Systematic Survey of Human Ear Biometrics Recognition by using 2D images 3

A Systematic Survey of Human Ear Biometrics Recognition by u...

引用

3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2023

作者： Elmarzaki, Howayda Abedallah Gargoum, Bothaina F. Abdo, Amina A University of Benghazi Department of Software Engineering Benghazi Libya University of Benghazi Department of Computer Science Benghazi Libya Collage of Computer Technology Department of Artificial Intelligence Benghazi Libya

ISBN: (纸本)9798350319897

Nowadays, one of the most serious issues is secure verification, especially with the advent of artificial intelligence and machine learning and deep learning algorithms. As a result, the research field of recognizing human biometrics has grown significantly, and various types of algorithms have been proposed to help with security. From the available literature, we noticed that researchers were focusing on various systematic surveys for the recognition of human biometrics, including faces, eyes, finger prints, palms, and etc. Despite the popularity of machine and deep learning methods in the field of human hearing prediction, surveys of such methods are rare. In this work, we present an up-to-date survey of the ear recognition field by using 2D images and display the most relevant papers from the most popular databases by applying different techniques based on several criteria. This review presents an overview of the similarities and differences between different works and provides a statistical analysis view of common techniques commonly used in ear recognition literature. The results presented in this report provide insight on the patterns of this field's research. © 2023 IEEE.

关键词： Biometrics

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Design & Implementation of Brain Controlled Interface For Healthcare Applications

Design & Implementation of Brain Controlled Interface For He...

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2023 IEEE international conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2023

作者： Prabhu, Nandan Shripada, B. Shetty, Athmik Gowda, Dashvanth Singh, Chandra Prameela, N.S. Sahaydri College of Engineering & Management Electronics & Communication Engineering Department Adyar Mangalore India

ISBN: (纸本)9798350340129

BCIs have gained significant attention as a means of communication and control for individuals with disabilities or neurological disorders. However, existing BCIs often suffer from limitations such as low accuracy, complexity, and limited functionality, hindering their widespread adoption in healthcare settings. This paper aims to address these limitations by developing a BCI system that incorporates advanced signal processing techniques, machine learning algorithms, and intuitive user interfaces.A functional prototype of the BCI system is developed, integrating appropriate hardware components and software algorithms. Extensive testing and validation are conducted to assess accuracy, reliability, safety, and adaptability, with user feedback gathered through surveys and usability studies to improve user satisfaction. © 2023 IEEE.

关键词： Electroencephalography

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Extracting Relevant Features for software Transplantation 5th

Extracting Relevant Features for Software Transplantation

引用

5th international conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Sodhi, Gurjot Singh Rattan, Dhavleesh Department of Computer Science and Engineering Punjabi University Punjab Patiala147002 India Koenig Solutions Pvt. Ltd. Gurugram India

ISBN: (纸本)9789819716814

The creation of intelligent software depends on the ability to transfer software without any restrictions. In this article, a crucial stage in software engineering, the feature extraction technique for effective software transplantation, is discussed. As hardware, operating systems, or other factors change, it is commonly necessary to move software from one environment to another. This process is known as "software transplanting." It is vital to identify and extract the relevant software characteristics, which might be challenging given how complex software is, in order to carry out efficient software transplanting. On the other hand, the procedure to extract these attributes from the software might be time-consuming and need extensive understanding. We provide a feature extraction method that makes use of machine learning methods to extract beneficial components from software in order to tackle this problem. Our approach includes identifying essential software components, including variables, functions, and other elements. We next use these components to train our machine learning model to separate significant properties. We use a dataset of software applications to compare the results of our strategy to those obtained using manual feature extraction methods. Our results demonstrate that our technique effectively extracts crucial traits and can significantly reduce the time and effort required for successful software transplanting. Thus, by making software transplantation more effective and successful, the recommended feature extraction approach greatly advances the field of software engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction 20

Graph Neural Network vs. Large Language Model: A Comparative...

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20th international conference on Predictive Models and Data Analytics in software engineering (PROMISE)

作者： Acharya, Jagrit Ginde, Gouri Univ Calgary Calgary AB Canada

ISBN: (纸本)9798400706752

A vast number of incoming bug reports demand effective methods to identify priority and severity for bug triaging. With increased technological advancement, machine learning and deep learning have been extensively examined to address this problem. Although Large Language Models (LLMs) such as Fine-tuned BERT (early generation LLM) have proven to capture context in the underlying textual data, severity and priority prediction demand additional features for understanding the relationships with other bug reports. This work utilizes the graph-based approach to model the bug reports and their other attributes, such as component, product and bug type information. It utilizes the relational intelligence of Graph Neural Network (GNN) to address the prioritization and severity assessment of bug reports in the Bugzilla bug tracking system. Initial tests on the Mozilla project dataset indicate that a project-wise predictive approach using GNNs yields higher accuracy in determining the priority and severity of bug reports compared to LLMs across multiple Mozilla projects, contributing to a notable advancement in the automation of bug severity and priority prediction tasks. Specifically, GNNs demonstrated a remarkable improvement over LLMs, increasing the priority prediction accuracy by 37% & 30% and severity prediction accuracy by 43% & 30% for Core and Firefox projects, respectively. Overall, GNN outperformed the Fine-tuned BERT (LLM) in predicting priority and severity for all the Mozilla projects.

关键词： Requirement engineering Large Language Model BERT Graph Neural Networks Natural Language Processing

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Optimizing DDoS Attack Classification in SDN Networking via Combining Features

Optimizing DDoS Attack Classification in SDN Networking via ...

引用

2023 international conference on engineering, Applied and Nano Sciences, ICEANS 2023

作者： Nasser, Aula Riyadh Abdullah, Mahmood Zaki College of Engineering Mustansiriyah University Computer Engineering Department Baghdad Iraq

ISBN: (纸本)9798350341973

software Defined Networking has evolved as a transformative networking paradigm, enabling the dynamic programmability of network devices and redefining traditional network architecture. This paper addresses the imperative need for effective SDN security, particularly in countering Distributed Denial of Service attacks, which exploit the inherent flexibility of SDN to inflict widespread network disruption. The core goal of this research is to leverage machine learning classifiers-namely Decision Tree, Naive-Bayes, K Nearest Neighbors, and Random Tree-to discern and classify malicious and normal packets within an SDN dataset. To enhance the accuracy of classification, a comprehensive feature selection process is executed, encompassing four distinct techniques: Information gain, gain ratio, ReliefF, and symmetrical uncertainty. The pivotal contribution of this paper lies in the refinement of attribute selection through a combining approach, pinpointing the most salient attributes from the previously identified set. Additionally, a contemporary SDN dataset is employed, specifically tailored to identify distributed denial of service attacks within an SDN environment. The results demonstrate that the combined approach outperforms other methods with an exceptional accuracy of 99.9979% with the Decision Tree algorithm, emphasizing the practical applicability of this methodology. The study employs a diverse array of evaluation criteria, encompassing Precision, Recall, F-measure, and Accuracy, as indicators of performance, to evaluate the model's effectiveness. This research underscores the critical significance of SDN security and showcases the effectiveness of machine learning in countering DDoS threats within SDN networks. Through the innovative fusion of feature selection techniques and advanced classifiers, it delivers substantial enhancements to SDN security, promising robust protection against DDoS attacks within SDN environments. © 2023 IEEE.

关键词： Adversarial machine learning

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INTRUSION PREDICTION USING machine learning TECHNIQUES

INTRUSION PREDICTION USING MACHINE LEARNING TECHNIQUES

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Priya, D. Divya Kiran, Ajmeera Valaboju, Sabitha Ramesh, Nagaram Paulraj, D. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India CVR College of Engineering Hyderabad501510 India Department of Information Technology BVRIT Narsapur502313 India Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India

ISBN: (纸本)9798350305708

In this day and age, substantial advances in the realm of security are being achieved at a quick pace. Because of the unexpected increase in the number of internet users, there is an increase in issues connected to hardware failure, software deployment failure, and memory allocation, which directly or indirectly results in data loss. [1]Network invasions entail the theft of important network resources, usually always through exploiting flaws in the group of networks and/or their data. Several hardware, software, and network elements can all contribute to network intrusion. Ineffective communication protocols, certain devices following their own protocol, proper file size constraints not defined, and poor firing are all software factors. Outdated routers, too many devices connected to a single router, and incompatible hardware are all examples of hardware factors. Network hacking, bad network architecture, and oversubscription are examples of unrelated factors. Our study intends to analyse the dataset, which includes all of the aforementioned parameters, using Ad boost classification, Stacking classification, Random Forest classifier, and Gaussian Nave Bayes Classifier. © 2023 IEEE.

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Requirements engineering for Automotive Perception Systems: An Interview Study 1

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29th international Working conference on Requirements engineering - Foundation for software Quality (REFSQ)

作者： Habibullah, Khan Mohammad Heyn, Hans-Martin Gay, Gregory Horkoff, Jennifer Knauss, Eric Borg, Markus Knauss, Alessia Sivencrona, Hakan Li, Jing Chalmers Univ Gothenburg Gothenburg Sweden Lund Univ Lund Sweden Zenseact AB Gothenburg Sweden Kognic AB Gothenburg Sweden

ISBN: (数字)9783031297861

ISBN: (纸本)9783031297854;9783031297861

Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that practitioners experience in this domain. Method: We have conducted an interview study with 19 participants across five companies and performed thematic analysis. Results: Practitioners have difficulty specifying upfront requirements, and often rely on scenarios and operational design domains (ODDs) as RE artifacts. Challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Conclusions: Our findings contribute to understanding how RE is practiced for DAS perception systems and the collected challenges can drive future research for DAS and other ML-enabled systems.

关键词： machine learning Requirements engineering Perception systems Driving automation systems Autonomous driving

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