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Performance Comparison of Breathing Signal Classifiers Using Machine learning Techniques 14

Performance Comparison of Breathing Signal Classifiers Using...

引用

14th IEEE international conference on Control System, computing and engineering, ICCSCE 2024

作者： Razman, Nur Fatin Shazwani Nor Nasir, Haslinah Mohd Zainuddin, Suraya Brahin, Noor Mohd Ariff Mispan, Mohd Syafiq Pasya, Idnin Wahab, Nur Haliza Abdul Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka76100 Malaysia The University of Aizu Aizu-Wakamatsu Tsuruga Ikki-Machi Fukushima965-8580 Japan Skudai Johor81310 Malaysia

ISBN: (纸本)9798350364507

One of the most important indicators of a person's health status is their rate of respiration, which is why clinical exams should closely monitor it. In the medical field, respiratory signal classification plays a vital role in the diagnosis and management of a range of respiratory conditions. However, effectively categorising respiratory signals remains a considerable issue due to the complexity and diversity of these signals. This study presents a thorough analysis of machine learning models for the categorization of respiratory signals, using a comparative method to assess each model's performance. A variety of machine learning approaches, encompassing both conventional techniques investigated such as Random Forest, Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) to see how well the algorithm classifies the respiratory signals. Processing procedures, data modeling, data classification, and data analysis used in the classification process are all included in the paper. The findings of each model in terms of classification accuracy, computational efficiency, and interpretability are examined through thorough testing on benchmark datasets. SVM was found to execute 92% more accurately than the other ML technique. This study can be expanded to compare with the various deep learning approaches to obtain better classification capable of real time application. © 2024 IEEE.

关键词： Contrastive learning

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A COMPARATIVE ANALYSIS OF DESIGN AND MANUFACTURE teaching IN MECHANICAL engineering 25

A COMPARATIVE ANALYSIS OF DESIGN AND MANUFACTURE TEACHING IN...

引用

25TH international conference ON engineering AND PRODUCT DESIGN EDUCATION (E&PDE)

作者： Masen, Marc Butt, Ahmed Tamkin Agg, Chloe Imperial Coll London Dept Mech Engn London England Nottingham Trent Univ Dept Engn Nottingham England

ISBN: (纸本)9781912254194

This paper draws parallels and contrasts between the Design and Manufacture (D+M) focussed learning tracks of the Mechanical engineering courses at Nottingham Trent University (NTU) and Imperial College London (ICL). These two institutions have historically had a different focus and vision. At NTU, various engineering courses undergo the same D+M module with the aim of delivering well-rounded engineers who have specialised within their own discipline and have acquired skills and knowledge in areas that are considered slightly outside their domain of study. D+M teaching is approached as a tool to encourage creativity across disciplines, within the themes of sustainability and robust product development. The objective is to remove inter-disciplinary barriers with the appreciation that problems of the present and future require pragmatic solutions from creative problem-solvers who are not limited by their disciplines of study. The Mechanical engineering course at Imperial has a strong emphasis on theoretical and mathematical foundations, with D+M modules aiming to integrate knowledge obtained and to bring this theoretical knowledge into practice. Additionally, the students achieve competence in engineering drawing, standards, design methodologies, and workshop skills, as well as transferrable skills. The objective is to develop mechanical engineers who combine strong analytical foundations with innovative product development skills. Based on a comparative analysis of the two programmes, a two-axis digital/practical-breadth/depth map and a learning outcome map have been developed. These can enable D+M Module Leaders and Course Directors at different institutions to make more informed decisions about teaching, content, delivery, and the student journey.

关键词： Design and manufacture learning outcomes pedagogies

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Image Inpainting Using Deep learning Techniques: A Review 5

Image Inpainting Using Deep Learning Techniques: A Review

引用

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

作者： Haritha, L. Prajith, C.A. College of Engineering Trivandrum Department of Electronics and Communication Trivandrum India

ISBN: (纸本)9798350334128

Image inpainting is a significant research area in the field of computer vision, with a diverse range of applications in image processing. Traditional image inpainting techniques proved to ineffective in generating better inpainted results, as they tend to produce inconsistent and multiple image semantics. Due to the advancement of Deep learning (DL) in recent years, this can be employed to address the issues with conventional image inpainting techniques. Currently, researches are mainly focused on image inpainting techniques based on deep learning using CNN, GAN etc. In this article, several deep learning based image inpainting techniques are been reviewed. © 2023 IEEE.

关键词： Deep learning

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Application of Advanced AI Algorithms for Fintech Crime Detection 15

Application of Advanced AI Algorithms for Fintech Crime Dete...

引用

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

作者： Sharma, Preeti Prakash, A. Shanker Malhotra, Anjul USB Commerce Chandigarh University Mohali India Saheed Anurup Chandra Mahavidyalaya West Bengal South 24 Parganas Burul India Chandigarh India

ISBN: (纸本)9798350370249

Considering the formidable challenge of detecting financial crimes, particularly money laundering, in today's global context, we have proposed methodology combining supervised and unsupervised machine learning (AI) techniques to identify suspicious transactions with minimal human intervention. Key contributions include enhanced data pre-processing, auto-labelling, and anomaly detection to improve detection accuracy and reduce false positives. © 2024 IEEE.

关键词： Artificial Intelligence Feature engineering Financial Crime Detection Financial service Money Laundering

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Design of a Hybrid Framework for Detecting Attacks in Fog-based IoT Architecture using Deep learning 15

Design of a Hybrid Framework for Detecting Attacks in Fog-ba...

引用

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

作者： Pokale, Navnath B. Sharma, Pooja Mane, Deepak T. Dept. of Computer Engineering School of Engineering and Technology D. Y. Patil University Ambi Pune410507 India Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331300579

IoT-Fog computing offers a broad variety of services for IoT-based end-systems. End IoT devices communicate with cloud nodes and fog nodes to administer client tasks. During the data collection process between the fog and cloud layers, IoT endpoints are more likely to be compromised by critical attacks such as DDoS and other security threats. It is necessary to detect these network vulnerabilities early on. By extracting features and placing the danger in the network, DL is crucial in predicting end-user behavior. Due to the limited processing power and less storage, Deep learning algorithms cannot be directly implemented on IoT devices. In this article, we present a three stage Deep Hybrid Detection Model for Attack Detection in IoT-Fog Architecture. Enhanced Z-score normalization based data preparation will be carried out in the beginning. On the basis of preprocessed data, features such as IG, raw data, entropy, and enhanced MI are extracted in the second step. The collected characteristics are used as an input to hybrid classifiers called DBN and optimized DeepMaxout in the third stage to classify the assaults based on the input dataset. A hybrid optimization model known as the BMJFO (Blue Monkey Jelly Fish Optimization) algorithm is presented for the best DeepMaxout training. The suggested model produced increased accuracy, precision, sensitivity, and specificity. The suggested model produced superior accuracy, precision, sensitivity, and specificity of 95.80%, 94.94%, 96.38%, and 97.94%, respectively. © Grenze Scientific Society, 2024.

关键词： Fog computing

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Enhancing Semantic Segmentation Through a Targeted Approach to Hard-to-Classify Examples 9

Enhancing Semantic Segmentation Through a Targeted Approach ...

引用

9th IEEE international conference on Progress in Informatics and computing, PIC 2024

作者： Padalkar, Ganesh Ravindra Khambete, Madhuri B. MKSSS's Cummins College of Engineering for Women Department of Electronics and Telecommunications Pune India

ISBN: (纸本)9798350363203

This research paper examines the effectiveness of semantic segmentation utilizing a weighted cross-entropy loss function. A modified version of cross-entropy loss is implemented to address data imbalance and improve predictions for hard-to-classify classes. Hyperparameters of the deep learning model are adjusted based on this modified loss function. The performance of the proposed loss function is evaluated through standard metrics. © 2024 IEEE.

关键词： Semantic Segmentation

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Hermes - Smart College Application using Android 2

Hermes - Smart College Application using Android

引用

2nd international conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Dimlo, U.M. Fernandes SilpaRaj, M. Srilakshmi, Ch. Kamesh, D.B.K. Balaram, Allam Sreyas Institute of Engineering and Technology Department of Cse India India Vnr Vignana Jyothi Institute of Enginnering and Technology Department of It Hyderabad India Malla Reddy Engineering College for Women Department of Cse India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350370188

Android apps emerged with modern technology. It's time to ditch websites for apps. We are thrilled to debut the Smart-College app, which will act like a condensed college website. The teaching staff and parents of students in their classrooms will use this app to track attendance, grades, and campus club activity. This tool helps colleges, students, and parents communicate more closely. Logging in with their credentials lets parents track their child's progress via the app. Teachers can record attendance using the programme, and if a student is absent, parents' mobile devices will be notified promptly. This project aims to reduce parents' academic responsibilities. This app was developed in Android Studios and deployed on AWS Server. © 2024 IEEE.

关键词： Studios

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Construction and Application of Digital College English teaching Resources on the Basis of Data Mining 3rd

Construction and Application of Digital College English Teac...

引用

3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Yang, Xiaomin Guilin University School of Language and Literature Guangxi541002 China

ISBN: (纸本)9783031631290

The arrival of the digital era has brought many challenges and changes to traditional teaching models, teaching methods, and teaching resources in higher education. Among them, the construction and application of digital educational resources has also become an important trend in the future development of higher education. This article mainly introduces the construction and application of digital college English teaching resources based on data mining. In terms of the construction of teaching resources, this paper uses the method based on data mining technology to analyze and process a large number of student learning data and teacher teaching data within a certain period of time, mine the patterns and rules of student learning, and establish knowledge expressions, Knowledge graph and curriculum knowledge trees related to the curriculum;In terms of application, online teaching platforms are used to provide students with rich and diverse digital teaching resources, allowing them to achieve targeted learning through various social learning methods such as independent learning, autonomous dialogue, and group discussions. Practice has proven that digital college English teaching resources based on data mining can better serve the teaching practice of higher education. On the one hand, this teaching resource makes students’ learning more targeted and personalized, which is not only conducive to interaction between teachers and students, but also helps to improve students’ English learning efficiency;On the other hand, this teaching resource can provide data support and reference for teachers’ teaching practice, so as to better grasp students’ learning situation and teaching progress, and achieve optimization and improvement of the teaching process. In summary, the digital college English teaching resources proposed in this article based on data mining are a feasible and effective teaching model teaching. Based on this model, we can continuously optimize teaching content,

关键词： Students

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The University-industry Collaborative Training Strategy in "digital medical image processing" Course 24

The University-industry Collaborative Training Strategy in "...

引用

2024 international conference on Artificial Intelligence and Teacher Education

作者： Wu, Huiqun Ji, Dafeng Geng, Xinyun Li, Zhaotong Ni, Xuejun Lv, Guangming Nantong Univ Dept Med Informat Nantong Peoples R China Nantong Univ Affiliated Hosp Dept Ultrasonog Nantong Peoples R China Nantong Univ Dept Anat Nantong Peoples R China

ISBN: (纸本)9798400710131

The curriculum system of intelligent medical engineering (IME) students covers various aspects such as medicine, engineering, and information technology. Among them, the course of "digital medical image processing" is of great significance for cultivating the skills of medical image data processing required by the profession. In the context of "University-industry collaboration", it is of great significance to establish an education system that can effectively integrate the resources of the intelligent medical industry, education, and society, and cultivate talents that meet the needs of the intelligent medical industry. This article explores the reform ideas of this course and aims to integrate hospitals, universities, and enterprises to form a university-industry collaborative education model, with the goal of cultivating students to acquire comprehensive knowledge and skills and to apply their knowledge to solve practical problems. In short, this system aims to provide reference for the talent cultivation of IME professionals by promoting cooperation and communication between hospitals, universities, and enterprises.

关键词： Intelligent medical engineering information technology collaborative education

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Cloud computing Security: A Comprehensive Review of Machine learning Algorithms 2

Cloud Computing Security: A Comprehensive Review of Machine ...

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2nd IEEE international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Alsalami, Zaid Salim, Mohammed Hussein Al-Saidi, Imad Al-Din Alaak, H.H. Khaddour, Reem Hamdan Kareem Taaban, Nabaa Abdel Amir, Zahraa Naji The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Al-Farahidi University Baghdad Iraq Al-Kunooze University College Computer Engineer Department Basra Iraq The University of Mashreq Baghdad10021 Iraq Mazaya University College Thi-Qar Iraq Kut University College Business Administration Department Wasit Al Kut52001 Iraq Uruk University Baghdad Iraq

ISBN: (纸本)9798350368109

Utilizing the cloud implies having organized assets, particularly information and administration, accessible at whatever point you would like them without requiring uncommon client courses of action. More as of late, CC has developed into a arrange of both open and private datacenters advertising clients a single online course of action. By putting preparing and information capacity closer to the end clients, edge computing may be a forward-thinking approach to computers that guarantees to speed up reaction times and spare transfer speed. Versatile phone applications are dispersed by means of versatile gadgets that give scattered computing. By the by, security concerns with CC and edge computing, such deciding client defenselessness and organization together declaration, make it troublesome to rapidly distinguish compute models. Machine learning is the consider of computational processes on computers that truly gotten to be way better with involvement. In this investigate extend, we offer an examination of CC security dangers, issues, and strategies that utilize one or more ML calculations. Fortification, semi-supervised, uncontrolled, and synchronized learning are among the obvious machine learning computations that are surveyed in arrange to fathom cloud security issues. Following, we compare the points of interest, drawbacks, and focuses of intrigued of each methodology to decide how viably it was executed. Furthermore, we select to investigate up and coming ponders centered on secure CC models. Since cloud computing offers unmatched adaptability, adaptability, and cost-efficiency, it has totally changed how businesses store, process, and oversee information. But this fast acknowledgment has moreover brought up genuine security issues, requiring solid and intelligent security strategies to defend basic information. With the improvement of advanced strategies for danger location, inconsistency recognizable proof, and interruption avoidance, machine learning (ML) a

关键词： Cloud security

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