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Deep automatic segmentation of brain tumours in interventional ultrasound data

Current Directions in Biomedical engineering 2022年第1期8卷 133-137页

作者： Zeineldin, Ramy A. Pollok, Alex Mangliers, Tim Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Research Group Computer Assisted Medicine Reutlingen University Reutlingen Germany Faculty of Electronic Engineering (FEE) Menoufia University Menouf Egypt

Intraoperative imaging can assist neurosurgeons to define brain tumours and other surrounding brain structures. Interventional ultrasound (iUS) is a convenient modality with fast scan times. However, iUS data may suffer from noise and artefacts which limit their interpretation during brain surgery. In this work, we use two deep learning networks, namely UNet and TransUNet, to make automatic and accurate segmentation of the brain tumour in iUS data. Experiments were conducted on a dataset of 27 iUS volumes. The outcomes show that using a transformer with UNet is advantageous providing an efficient segmentation modelling long-range dependencies between each iUS image. In particular, the enhanced TransUNet was able to predict cavity segmentation in iUS data with an inference rate of more than 125 FPS. These promising results suggest that deep learning networks can be successfully deployed to assist neurosurgeons in the operating room. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Ultrasonics

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

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Journal of Social Computing 2024年第2期5卷 180-193页

作者： Yanjing Li Xiaowei Wang Fukun Chen Bingxu Zhao Qiang Fu the Institute of Education Science Research Heilongjiang UniversityHarbin 150080China the School of Cyberspace Security Shandong University of Political Science and LawJinan 250014China the School of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the Faculty of Electrical and Computer Engineering University of VictoriaVictoriaV8P 5C2Canada. the Guangxi Key Lab of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information EngineeringGuangxi Normal UniversityGuilin 541004China

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of *** study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of technology during the 2012-2013 academic *** have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these *** goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures *** results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.

关键词： online learning learning outcomes prediction learning behavior analysis spiking neural network

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Development of an Open-Source and Low-Cost Robotic Arm based on STM32 Microcontroller for Promoting STEM Education

Development of an Open-Source and Low-Cost Robotic Arm based...

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2023 IEEE Symposium on computers and Informatics, ISCI 2023

作者： Asri, Fatin Atirah Mohd Ayob, Mohammad Afif Rohaziat, Nurasyeera Rahman, Hisyam Abdul Rahman, Khairul Azlan A. Ayob, Mohd Erdi Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Batu Pahat Parit Raja86400 Malaysia Uow Malaysia Kdu University College School of Engineering Selangor Shah Alam40150 Malaysia Faculty of Electronics and Computer Technology and Engineering Melaka Durian Tunggal76100 Malaysia

ISBN: (纸本)9798350302363

As a key component of Industry 4.0, robotics is acquiring traction in classrooms around the world. Robotics initiatives may stimulate students' interest in STEM (Science, technology, engineering, and Mathematics) subjects. Therefore, it is essential to translate theoretical information into tangible prototypes and study robotics using physical models. Most schools cannot afford to provide students with sufficient opportunities to work with robots due to the expense of robotics. This study aims to promote STEM education by describing the development of a free, open-source robot joint with three degrees of freedom (DOF). The control algorithm for a 3D-printed MK2 robot arm was programmed with an STM32 microcontroller. The robot has a servo-powered arm that has a gripper on the end-effector. A joystick can be used to control the movement of any mechanical joint. The forward kinematic has also been constructed and validated in a simulation-based environment in MATLAB and SolidWorks. The developed kinematics algorithm showed precise Denavit-Hartenberg (DH) characteristics with high accuracy with the worst-case scenario for the kinematic computation error contributes to only 0.26% or 0.0702mm of the end-effector position. © 2023 IEEE.

关键词： Kinematics

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A new dataset and systematic evaluation of deep learning models for student activity recognition from classroom videos

A new dataset and systematic evaluation of deep learning mod...

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2022 International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2022

作者： Nguyen, Phuong-Dung Nguyen, Hong-Quan Nguyen, Thuy-Binh Le, Thi-Lan Tran, Thanh-Hai Vu, Hai Huu, Quynh Nguyen Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Viet-Hung Industrial University Faculty of Information Technology Hanoi Viet Nam University of Transport and Communications Faculty of Electrical-Electronic Engineering Hanoi Viet Nam Thuyloi University Faculty of Computer Science and Engineering Hanoi Viet Nam

ISBN: (数字)9781665474108

ISBN: (纸本)9781665474108

Student activity, a non-verbal modality, is an important cue to imply the students' learning state and engagement level during class. Quantitative observation of the student activities will contribute to innovating teaching methods. However, traditional methods that rely on manual observation are time-consuming. Nowadays, surveillance cameras have been widely deployed in the classroom. This results in a massive amount of classroom videos. However, the exploitation of these videos is limited due to the lack of annotated information. This paper has two main contributions. First, a fully annotated dataset dedicated to student activity analysis has been proposed. To this end, five activities in the classroom context including sitting, standing, sleeping, raising-hand and using-phone have been defined and collected in different class sessions. Second, three deep learning models have been fully assessed for the activities recognition. © 2022 IEEE.

关键词： Object detection

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A New Perspective on Time and Gravity

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Journal of High Energy Physics, Gravitation and Cosmology 2024年第1期10卷 346-362页

作者： Ittipat Roopkom Wirote Jongchanachavawat Chermdhong Prattanaruk Kwanchai Nanan Pichet Wisartpong Thawatchai Mayteevarunyoo Paramote Wardkein Faculty of Engineering and Industrial Technology Phetchaburi Rajabhat University Phetchaburi Thailand School of Electrical and Electronic Engineering Mahanakorn University of Technology Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Naresuan University Phitsanulok Thailand Department of Telecommunications Engineering School of Engineering King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

This paper presents a hypothesis regarding the existence of time fused in spacetime, assuming that time possesses the properties of both a particle and a field. This duality is referred to as the field-particle of time (FPT). The analysis shows that when the FPT moves through matter, it causes time dilation. The FPT is also a significant element that appears in relativistic kinetic energy (KE = (γ - 1) · mc²). Accelerating matter to near the speed of light requires relativistic energy approaching infinity, which corresponds to the relativistic kinetic energy. Meanwhile, the potential energy (PE = mc²) from the rest mass remains constant. Then, the mass-energy equation can be rearranged in terms of PE and KE, as shown in E = (1 + (γ - 1)) · mc². The relativistic energy of the FPT also directly affects the gravitational attraction of matter. It transfers energy to each other through spacetime. The analysis demonstrates that the gravitational force is inversely proportional to the distance squared, following Newton’s law of gravity, and it varies with the relative velocity of matter. The relationship equation between relative time and the gravitational constant indicates that a higher intensity of the gravitational field leads to a slower reference time for matter, in accordance with the general theory of relativity. A thought experiment presents a comparison of two atomic clocks placed in different locations. The first one is placed in a room temperature, around 25°C, on the surface of the Earth, and the second one is placed in high-density areas. The analysis, considering the presence of the FPT, shows that the reference time slows down in high-density areas. Therefore, the second clock must be noticeably slower than the first one, indicating the existence of the FPT passing through both atomic clocks at different speeds.

关键词： Field-Particle of Time (FPT) Reference Time Relativistic Mass and Energy of FPT Gravity

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Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular–Structural–Functional Co-alterations

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Research 2024年第1期2023卷 471-487页

作者： Lin Jiang Yueheng Peng Runyang He Qingqing Yang Chanlin Yi Yuqin Li Bin Zhu Yajing Si Tao Zhang Bharat B.Biswal Dezhong Yao Lan Xiong Fali Li Peng Xu The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Life Science and Technology Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Psychology Xinxiang Medical UniversityXinxiang 453003China School of Science Xihua UniversityChengdu 610039China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJUSA School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001China Research Unit of NeuroInformation Chinese Academy of Medical Sciences2019RU035ChengduChina Montreal Neurological Institute and Hospital McGill UniversityMontrealQCCanada Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina Radiation Oncology Key Laboratory of Sichuan Province 610041 ChengduChina Rehabilitation Center Qilu Hospital of Shandong UniversityJinan 250012China

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain *** to lacking an effective approach to quantifying the covarying of structure and functional responses,how the structural–functional circuits interact and how genes encode the relationships,to deepen our knowledge of human cognition and disease,are still ***,we propose a multimodal covariance network(MCN)construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single *** further explored the potential association between brain-wide gene expression patterns and structural–functional covarying in individuals involved in a gambling task and individuals with major depression disorder(MDD),adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent *** analysis showed a replicable cortical structural–functional fine map in healthy individuals,and the expression of cognition-and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN *** analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences.

关键词： alterations evoked spatially

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Real-Time Multi-Class Infection Classification for Respiratory Diseases

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computers, Materials & Continua 2022年第11期73卷 4157-4177页

作者： Ahmed El.Shafee Walid El-Shafai Abdulaziz Alarifi Mohammed Amoon Aman Singh Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian UniversityGizaEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Higher Polytechnic School Universidad Europea del AtlánticoSantander39011Spain Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

Real-time disease prediction has emerged as the main focus of study in the field of computerized *** disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable,consistent,and timely,successfully lowering mortality rates,particularly during endemics and *** prevent this pandemic’s rapid and widespread,it is vital to quickly identify,confine,and treat affected *** need for auxiliary computer-aided diagnostic(CAD)systems has *** recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 *** advanced convolutional neural network(CNN)architectures in conjunction with radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible *** research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona *** purpose of this study is to offer a two-way COVID-19(2WCD)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2WCD has applied modifications to pre-trained models for better *** is designed and implemented to improve the generalization ability of the classifier for binary and multi-class *** with the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color *** proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic *** can also be used to forecast other lung-related *** the system can assist medical practitioner

关键词： COVID-19 real-time computerized disease prediction intelligent disease identification framework CAD systems X-rays CT-scans CNN real-time detection of COVID-19 infections

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Leveraging Channel Capacity of a Macro Diversity MIMO System in Gamma-Shadowed Nakagami-m Fading Channel for QoS estimation using Quantum Machine Learning 30

Leveraging Channel Capacity of a Macro Diversity MIMO System...

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30th Telecommunications Forum, TELFOR 2022

作者： Suljovic, Suad Vujovic, Vuk Markovic, Filip Milic, Dejan Petrovic, Nenad Lazarevic, Angelina Academy of Technical Vocational Studies Applied Engineering Sciences Belgrade Serbia MB University Computer Science and Informatics Information Technology Belgrade Serbia University of Prišrina Faculty of Technical Sciences Computer Science and Informatics Mitrovica Serbia University of Niš Telecommunications Faculty of Electronic Engineering Niš Serbia University of Niš Faculty of Electronic Engineering Computer Science and Informatics Niš Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

In this paper we consider a system of one macro diversity SC receiver (MD SC) and two micro MRC (mD MRC) receivers, operating in a Nakagami-m multipath environment with gamma shadowing. The combination of the maximum L-branch SNR signal-to-noise ratio is realized at the micro level, and the selector combiner (SC) with two base stations is realized at the macro level. A closed-form expression for the channel capacity (CC) of the envelope of the SC macro diversity output signal is calculated. Graphical results show the effects of different system parameters on system performance, as well as the improvement due to the use of a combination of the micro and macro diversity. The derived expressions are used within GPU-based mobile network for modeling, planning and simulation of the system to estimate the quality of service (QoS). © 2022 IEEE.

关键词： Machine learning

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Optimization Techniques in University Timetabling Problem:Constraints,Methodologies,Benchmarks,and Open Issues

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computers, Materials & Continua 2023年第3期74卷 6461-6484页

作者： Abeer Bashab Ashraf Osman Ibrahim Ibrahim Abakar Tarigo Hashem Karan Aggarwal Fadhil Mukhlif Fuad A.Ghaleb Abdelzahir Abdelmaboud Faculty of Computer Science&Information Technology Alzaiem Alazhari University13311KhartoumSudan Faculty Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400SabahMalaysia Department of Computer Science University of SharjahSharjah27272United Arab Emirates Electronic and Communication Engineering Department Maharishi Markandeshwar Deemed to be UniversityMullanaAmbala133207HaryanaIndia Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi Malaysia81310JohorMalaysia Department of Information Systems King Khaled UniversityMuhayel61913AseerSaudi Arabia

University timetabling problems are a yearly challenging task and are faced repeatedly each *** problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they can be solved through optimization algorithms to produce the aspired optimal *** techniques have been used to solve university timetabling problems,and most of them use optimization *** paper provides a comprehensive review of the most recent studies dealing with concepts,methodologies,optimization,benchmarks,and open issues of university timetabling *** comprehensive review starts by presenting the essence of university timetabling as NP-COP,defining and clarifying the two formed classes of university timetabling:University Course Timetabling and University Examination Timetabling,illustrating the adopted algorithms for solving such a problem,elaborating the university timetabling constraints to be considered achieving the optimal timetable,and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the *** is noted that meta-heuristic methodologies are widely used in the ***,recently,multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling ***,trends and future directions in university timetabling problems are *** paper provides good information for students,researchers,and specialists interested in this area of *** challenges and possibilities for future research prospects are also explored.

关键词： University timetabling timetabling approaches meta-heuristics combinatorial optimization

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