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Research on Unsupervised Campus Audio Anomaly Detection Based on Generative Adversarial Network 2

Research on Unsupervised Campus Audio Anomaly Detection Base...

引用

2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Xin, Yang Chen, Liang Liu, Wenhong Shenyang Ligong University College of Automation and Electrical Engineering Shenyang China Northeastern University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

In order to detect inappropriate speech in classrooms, this paper proposes an unsupervised model based on generative adversarial networks (GAN), which integrates an autoencoder structure to detect anomalies in uncalibrated classroom audio, saving a lot of labeling time. Enhance data by adding Latent Space Pitting and weight sharing. Meanwhile, due to the previous model being mostly used for two-dimensional image data, in order to enhance the learning ability of one-dimensional speech information features, a multi head attention mechanism was introduced. Experiments were conducted on a self-made classroom audio dataset, demonstrating that the proposed method can improve the ability of anomaly detection and effectively distinguish anomalies. © 2024 IEEE.

关键词： Generative adversarial networks

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Ear Biometric: A Deep learning Approach 5

Ear Biometric: A Deep Learning Approach

引用

5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Roy, Pritha Singha Kukreja, Vinay Jain, Vishal Vats, Satvik Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Sharada School of Engineering and Technology Sharda University UP Greater Noida India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350321425

CNN approach using deep learning had been applied in the current study for the detection of ear biometrics. Real-time ear pictures from Google were trained in the deep CNN model to conduct this experiment. The dataset was initially improved, preprocessed, and divided for training and validation by using MATLAB. For the training and validation, the dataset is split in the ratio of 80:20. The obtained datasets were also used to train and validate the deep CNN model. Following the implementation of the deep CNN model, the total accuracy acquired was 99.45%. This experiment was conducted to raise research awareness on biometrics and to create a useful model for ear biometrics. © 2023 IEEE.

关键词： Deep learning

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Robust Control of Linear Systems: A Min-Max Reinforcement learning Formulation 20

Robust Control of Linear Systems: A Min-Max Reinforcement Le...

引用

20th international conference on Electrical engineering, computing Science and Automatic Control, CCE 2023

作者： Flores-Campos, Juan Alejandro Perrusquía, Adolfo Instituto Politécnico Nacional UPIITA-IPN Mexico City Mexico Cranfield University School of Aerospace Transport and Manufacturing Bedford United Kingdom

ISBN: (纸本)9798350306767

In this paper, an online robust controller based on a min-max reinforcement learning approach for linear systems is discussed. Disturbances are represented by external signals coupled with the control input which are assumed to be bounded within a set of admissible disturbances. The proposed controller implements a min-max approach which realizes a smooth transition between optimal and robust controllers. Lyapunov stability theory is used to assess the stability and boundedness of the min-max robust formulation. A neural reinforcement learning architecture is used to obtain an approximation of the parameters associated to the optimal cost. Simulations are carried out to validate the proposed approach. © 2023 IEEE.

关键词： Linear systems

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Intelligent Underwater Sound Surveillance for Intrusion Detection and Emergency Alerting 15

Intelligent Underwater Sound Surveillance for Intrusion Dete...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohite, Vaishnavi Shivaji Patra, Krishna Gayatri Sankar, Ch Hari Srinivas, M. Ullas, S. Amrita School of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

With the demand of protective necessity of underwater environment and infrastructures, it is high time for the technological advancement towards intelligently monitoring and ensuring responsive security mechanism. A novel approach uses modern signal analysis and machine learning methods for the differentiation and subsequent analysis of underwater sounds based on the specific source, focusing on interfering objects like submarines, ships, or other noise caused by humans. The introduced system entails the use of the real-time monitoring system so as to allow constant assessment of the acoustic data collected beneath the water. In case of exposure to any abnormal sounds, an alarm system is activated meant to inform a monitoring and control centre. Its versatility for various underwater conditions is addressed by exercising of the system that considers the fluctuations of water temperature, pressure, and acoustic background. The state centralized monitoring of the system makes it easy for the system to respond to the potential threats, faster than using the graphical user interface. The following sections of this paper describe both the general system design and the architecture and feature extraction of the proposed machine learning algorithms, specific experimental test results and where appropriate demonstrations of the specific features and an assessment of the envisioned role of this system in underwater security and emergency alerting. © 2024 IEEE.

关键词： Emergency Alerting Machine learning for Underwater Surveillance Underwater Acoustic Audio Processing

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Reinforcement learning for Robot Navigation 15

Reinforcement Learning for Robot Navigation

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Suyash, Kumar Kumar, Pratyush Dubey, Ayush Kumar Tiwari, Shubh Tiwari, Abhishek Pavithra, G. Manjunath, T.C. VII Sem Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Dept. of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331300579

This paper undertakes a comprehensive exploration of the application of Reinforcement learning (RL) in robotic systems, with a specific focus on the navigation tasks carried out in intricate environments. The primary objective is to elevate the adaptability, efficiency, and generalization capabilities of robots. The study delivers an insightful overview of RL concepts, accentuating their relevance in the context of robot navigation. It systematically evaluates various RL algorithms, with a particular emphasis on Deep Reinforcement learning (DRL). The analysis incorporates crucial considerations such as state representation, action space, and reward shaping. In addition to scrutinizing RL algorithms, the paper delves into the integration of sensor data, mapping techniques, and Simultaneous Localization and Mapping (SLAM) within RL frameworks, aiming to enhance the robot's perception capabilities. The discussion extends to the adaptation of RL methodologies to address real-world challenges, including dynamic obstacles and unknown terrains. The paper substantiates its findings through the presentation of case studies and experiments that exemplify practical applications of RL in robotic navigation. The conclusion of the paper encapsulates a thoughtful discussion on existing challenges, potential future research directions, and anticipated advancements in the field. The overarching goal is to provide a comprehensive resource for researchers and practitioners interested in harnessing RL to augment autonomous robot navigation within complex and dynamic environments. By offering a thorough examination of RL applications and addressing practical challenges, this paper contributes significantly to the evolving landscape of robotics research and technology. © Grenze Scientific Society, 2024.

关键词： Mapping

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SLO-Aware DL Job Scheduling for Efficient FPGA-GPU Edge Cloud computing 23rd

SLO-Aware DL Job Scheduling for Efficient FPGA-GPU Edge Clou...

引用

23rd international conference on Web engineering (ICWE)

作者： Kim, Taewoo Jeon, Minsu Lee, Changha Kim, SeongHwan Al-Hazemi, Fawaz Youn, Chan-Hyun Korea Adv Inst Sci & Technol Daejeon 34141 South Korea Univ Jeddah Jeddah 21959 Saudi Arabia

ISBN: (纸本)9783031503849;9783031503856

Deep learning applications have become increasingly popular in recent years, leading to the development of specialized hardware accelerators such as FPGAs and GPUs. These accelerators provide significant performance gains over traditional CPUs, but their efficient utilization requires careful scheduling configuration for given DL requests. In this paper, we propose a SLO-aware DL job scheduling model for efficient FPGA-GPU edge cloud computing. The proposed model takes into account variant service-level objectives of the DL job and periodically updates the accelerator configuration of DL processing while minimizing computation costs accordingly. We first analyze the impact of various DL-related parameters on the performance of FPGA-GPU computing. We then propose a novel scheduling algorithm that considers the timevariant latency SLO constraints and periodically updates the scheduling configuration. We evaluated our scheduler using several DL workloads on a FPGA-GPU cluster. Our results demonstrated that our scheduler achieves improvements in terms of both energy consumption and SLO compliance compared to the traditional DL scheduling approach.

关键词： DL Scheduling FPGA-GPU computing Edge computing for DL Serving

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Multiclass Breast Cancer prediction from Mammogram using CNN 7

Multiclass Breast Cancer prediction from Mammogram using CNN

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Nallasivam, Manikanda Prabu Rajesh, K.P. Maurya Sharon, S. Megavathi, B. Nithish, S.P. Rajasankar, S. Sri Krishna College of Technology Electronics & Communication Engineering Coimbatore India

ISBN: (纸本)9798350372816

The breast cancer is a greatest threat among women across the globe. In the modern era of medical image analysis, the accurate identification of breast cancer from mammogram images is a critical task. This article proposes a methodology for developing a multiclass breast cancer classifier using deep learning architecture. The proposed methodology uses a sequential model architecture comprising Conv2D, MaxPooling2D, Flatten, Dropout and Dense layers. All these layers serve a specific role in feature extraction and classification. The model extracts appropriate features from mammogram images and progressively learns and discriminates representations to enhance classification accuracy. The incorporation of regularization techniques such as Dropout layers overcomes overfitting, improving the acquisition of robust features. ROC curve analysis is used in the compilation and training phases to evaluate the model performance. This article provides a systematic approach to the development and evaluation of a deep learning model for breast cancer identification. This also offers insights into its architectural framework, training process and performance assessment techniques. The proposed method has achieved classification accuracy of 98%. © 2024 IEEE.

关键词： Deep learning

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teaching Software Development for Real-World Problems using a Microservice-Based Collaborative Problem-Solving Approach 46

Teaching Software Development for Real-World Problems using ...

引用

ACM/IEEE 44th international conference on Software engineering: Software engineering Education and Training (ICSE-SEET)

作者： Lau, Yi Meng Koh, Christian Michael Jiang, Lingxiao Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (纸本)9798400704987

Experienced and skillful software developers are needed in organizations to develop software products effective for their business with shortened time-to-market. Such developers will not only need to code but also be able to work in teams and collaboratively solve real-world problems that organizations are facing. It is challenging for educators to nurture students to become such developers with strong technical, social, and cognitive skills. Towards addressing the challenge, this study presents a Collaborative Software Development Project Framework for a course that focuses on learning microservices architectures and developing a software application for a real-world business. Students get to work in teams to solve a real-world problem of their own choice. They are given opportunities to recognize that the software development process goes beyond writing code and that social and cognitive skills in engaging with each other are also essential. By adopting microservices architectures in the course, students learn to break down the functionalities of their applications into smaller pieces of code with standardized interfaces that can be developed, tested, and deployed independently. This not only helps students to learn various technical skills needed for developing and implementing the functionalities needed by the application in the form of microservices but also facilitates task allocation and coordination among their team members and provides a platform for them to solve problems collaboratively. Upon completion of their projects, students are also asked to reflect on their development process and encouraged to think beyond the basics for better software design and development approaches. The course curriculum incorporates the framework, especially for the student team projects. The earlier teaching weeks introduce a combination of concepts and lab exercises to students as the building blocks. The survey studies show that the framework is effective in enhancing th

关键词： software development collaborative problem-solving real-world solutions microservices architectures

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Designing an Independent Study to Create HPC learning Experiences for Undergraduates 29

Designing an Independent Study to Create HPC Learning Experi...

引用

29th IEEE international conference on High Performance computing, Data and Analytics (HiPC)

作者： Vargas-Perez, Sandino Kalamazoo Coll Dept Comp Sci Kalamazoo MI USA

ISBN: (纸本)9798350333886

This paper aims to present a multi-tiered approach to designing learning experiences in HPC for undergraduate students that significantly reinforce comprehension of CS topics while working with new concepts in parallel and distributed computing. The paper will detail the experience of students working in the design, construction, and testing of a computing cluster including budgeting, hardware purchase and setup, software installation and configuration, interconnection networks, communication, benchmarking, and running parallel code using MPI and OpenMP. The case study of building a relatively low-cost, small-scale computing cluster that can be used as a template for CS senior projects or independent studies, also yielded an opportunity to involve students in the creation of teaching tools for parallel computing at many levels of the CS curriculum.

关键词： Parallel computing HPC Undergraduate Education PDC teaching Tools OpenMP MPI Cluster

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Suraksha: Advanced SOS Android App with Intelligent Spam Alert Management and Collaborative Decision-Making 15

Suraksha: Advanced SOS Android App with Intelligent Spam Ale...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Padmavathi, A. Shashini, V. Ryan, J. Collin Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Safety is first and foremost in the current busy life, and smartphones give a helping hand in protecting. This paper has presented an Android app, Suraksha, which enhances personal safety during emergencies. This app ensures that assistance is sought immediately, whereas traditional ways require one to call a person or send the person a message or even unlock the phone, which may not happen when in distress. The nature of shrugs allows the app to differentiate a safe situation and a red alert. Through distress shrugs, the app sends the user's location and the user's defined message to the pre-registered ones. Suraksha app depends on humans to judge the alerts and all the information involved, which creates a bundle of evidence that will be sufficient. It is designed with a Machine learning model, which studies the user's learned things and the emergency type to understand its effectiveness. © 2024 IEEE.

关键词： Android Convulated neural networking Gesture GPS Isolation Forest Machine learning Registered Contacts Transmission protocols

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