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BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

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computer Modeling in engineering & Sciences 2022年第5期131卷 851-863页

作者： Yankun Cao Lina Xu Zhi Liu Xiaoyan Xiao Mingyu Wang Qin Li Hongji Xu Geng Yang School of Software Shandong UniversityJinan250101China School of Information Science and Engineering Shandong UniversityQingdao266237China School of Computer Science and Technology Shandong Jianzhu UniversityJinan250012China Department of Nephrology Qilu Hospital of Shandong UniversityJinan250012China State Key Laboratory of ASIC&Systems The School of MicroelectronicsFudan UniversityShanghai200433China Shenzhen Institute of Information Technology Shenzhen518172China

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary ***:In recent years,deep learning has been widely used in academia,industry,*** increasing number of companies are starting to recruit a large number of professionals in the field of deep *** numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis *** now,however,there is no practical training platform that can help radiologists learn automated medical image analysis ***:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this *** platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

关键词： BMR deep learning three dimensional medical decision support system deep learning engineer standard

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QSroute : A QoS Aware Routing Scheme for software Defined Networking

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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International Conference on software engineering and computer systems (ICSECS)

作者： Imran Edzereiq Kamarudin Mohamed Ariff Ameedeen Muhamad Idaham Umar Ong Azlee Zabidi Department of Computer Systems and Networking Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field.

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SER Analysis -An Approach to enhance Human Health

SER Analysis -An Approach to enhance Human Health

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Anusha N C.M. Mohana Akshat Sharma Aryan Sharma Garv Mittal Navadhesh. M Department of IoT School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore TamilNadu India Department of Software Systems SCOPE Vellore Institute of Technology Vellore Tamil Nadu India Department of Computational Intelligence SCOPE Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

This work focuses on Speech Emotion Recognition (SER) for extracting emotional states from speech signals, encompassing universal emotions like Happiness, Anger, Neutral, Calm, Sadness, Disgust, Fearful, and Surprised. The methodology involves extracting features such as Chromogram, Mel- frequency cepstral coefficients (MFCC), Mel scaled spectrogram, Tonal Centroid and Spectral contrast, followed by classification using Artificial Neural Networks (ANN). A health-oriented aspect, is integrated in this model by providing personalized guided meditation sessions based on the user's emotional state to promote mental wellness. The main aim of this work is to broaden its reach and impact through ongoing refinement of the model and exploration of new implementation strategies, ultimately contributing to a healthier and happier society. The results of this work demonstrate promising accuracy, with the trained deep neural network (DNN) model achieving approximately 81.25% accuracy in recognizing speech emotions in comparison with the existing algorithms. DNN shows a 11%, 8%, 14% increase in accuracy with RNN & LSTM, SVM, GMM respectively.

关键词： Support vector machines Emotion recognition Accuracy Speech recognition Speech enhancement Feature extraction Cognitive informatics Mel frequency cepstral coefficient Spectrogram Long short term memory

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Audio Adversarial Example Detection Using the Audio Style Transfer Learning Method

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IEEE Access 2023年 1-1页

作者： Kwon, Hyun Lee, Kangjun Ryu, Junyeol Lee, Jun Department of Artificial Intelligence and Data Science Korea Military Academy Seoul Republic of Korea Department of Software Science and Engineering Kunsan National University South Korea Department of systems & Mechanical Engineering Korea Military Academy Seoul Korea Division of Computer Information and Science Hoseo University Asan-si South Korea

In this paper, we propose a method for defending against audio adversarial examples that operates by applying audio style transfer learning. The proposed method has the effect of maintaining the classification result produced by the target model and removing the adversarial noise by changing only the style while maintaining the content of the input audio sample. In an experimental evaluation using the Mozilla Common Voice dataset as the test data source and TensorFlow as the machine learning library, the proposed method improved the target model’s accuracy on the adversarial examples from 2.1% to 79.2% while maintaining its accuracy on the original samples at 81.4%. Author

关键词： Speech recognition

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Development of a Query Delay Injection System for the MEC Simulator of the LWMECPS Platform

Development of a Query Delay Injection System for the MEC Si...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Ivan Filianin Vladimir Bogatyrev Matvey Lesonen Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

In this paper the problem of testing decision making systems for MEC platforms was formulated. Methods and means of organizing the introduction of network delays as part of the emulation system of MEC platforms LWMECPS were analyzed. Test applications mec-test-app and mec-test-client are developed for testing and evaluation of the introduced network delays. Developed mec-orch-app to provide test application management and metrics collection. Experiments have been conducted to estimate the insertion latency and manage the test applications using mec-orch-app. Special attention was paid to the organization of all parts of the system as a future laboratory bench for testing decision systems using reinforcement learning machine learning algorithms.

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Machine Learning Algorithms for Intrusion Detection Performance Evaluation and Comparative Analysis

Machine Learning Algorithms for Intrusion Detection Performa...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： B. Md. Irfan V. Poornima S Mohana Kumar Upendra Singh Aswal N. Krishnamoorthy Ramya Maranan Department of Information Technology Nalsar University of Law Hyderabad Telengana India Department of Computer Science VISTASPallavaram Chennai Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Software Systems and Engineering Vellore Institute of Technology Vellore Campus Vellore Tamilnadu Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection systems. This study sets out to do a thorough examination and comparison of the efficacy of several machine learning algorithms for use in intrusion detection. This research study evaluates the efficacy of several machine learning algorithms in correctly categorizing instances of network traffic as normal or invasive via extensive experiments performed on representative datasets. Algorithms like random forests, decision trees, SVMs, DL models and NNs are all being tested and rated. Effectiveness is measured and compared using a variety of performance indicators including accuracy, recall, precision, false positive rate, and F1-score. The results of this study emphasize the potential of deep learning models and Random Forests for use in intrusion detection and add to the body of knowledge around machine learning methods for this task. Professionals in the field of network security might use the results to their advantage when building intrusion detection systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable intrusion detection systems.

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Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz 2

Investigation of the Impact of Height and Bandwidth on Path ...

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Imam-Fulani, Yusuf Olayinka Faruk, Nasir Usman, Aliyu D. Abdulkarim, Abubakar Tekanyi, Abdoulie M.S Yaro, Abdulmalik S. Alozie, Emmanuel Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Abdullahi Baba, Bashir Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. University of Ilorin Department of Telecommunication Science Ilorin Nigeria Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Zaria Nigeria Kampala International University Department of Electrical and Telecommunication Engineering Kampala Uganda University of Hradec Králové Faculty of Informatics and Management Department of Informatics and Qualitative Methods Hradec Králové Czech Republic Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond. © 2023 IEEE.

关键词： 5G mobile communication systems

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract)

A Survey on Modern Deep Neural Network for Traffic Predictio...

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International Conference on Data engineering

作者： David Alexander Tedjopurnomo Zhifeng Bao Baihua Zheng Farhana Murtaza Choudhury A. K. Qin School of Computing Technologies RMIT University Australia School of Computing and Information Systems Singapore Management University Singapore School of Computing and Information Systems The University of Melbourne Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare; making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.

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Feasibility Study of a DC House Connected to a Conventional AC Distribution Network

Feasibility Study of a DC House Connected to a Conventional ...

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Compatibility in Power Electronics (CPE)

作者： Hossein Nourollahi Hokmabad Noman Shabir Victor Astapov Eduard Petlenkov Oleksandr Husev Juri Belikov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia FinEST Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798350318265

ISBN: (纸本)9798350318272

Leveraging advancements in power electronics, the adoption of Direct Current (dc) technology in net-Zero Energy Buildings (nZEBs) is seen as a promising approach to boost energy efficiency. Emerging dc technology aims to reduce power losses by eliminating unnecessary conversions between dc and Alternating Current (ac). This paper thoroughly assesses the effectiveness of dc and hybrid dc (partial dc topologies in comparison to conventional ac nZEBs. The hybrid solution integrates ac and dc networks and loads within the building, while the dc solution benefits from a purely dc internal electricity distribution network and exclusively dc loads. The study involves analyzing annual load profile data from 16 neighboring houses in Estonia, simulating their transition to nZEBs with dc, hybrid dc, and pure dctopologies. The study examines power conversion losses, the operational periods of power converters in relation to their maximum power capacity, and energy exchanges with the utility grid. Additionally, it explores the potential for creating energy communities based on the consumption patterns of these houses. The results indicate that pure dc nZEBs might not be as efficient as initially thought, especially when renewable resources are limited.

关键词： Measurement Renewable energy sources Power engineering Network topology Net zero Buildings Hybrid power systems

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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