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Early-Stage Segmentation and Characterization of Brain Tumor

computers, Materials & Continua 2022年第10期73卷 1001-1017页

作者： Syed Nauyan Rashid Muhammad Hanif Usman Habib Akhtar Khalil Omair Inam Hafeez Ur Rehman Department of Computer Science National University of Computer and Emerging Sciences IslamabadPeshawar CampusPakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopiPakistan IFahja Pvt Limited PeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadPakistan

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** average,patients do not survive 14 months after *** only way to minimize the impact of this inevitable disease is through early *** Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain *** manual classification of MRI scans takes a reasonable amount of time to classify brain *** this,dealing with MRI scans manually is also cumbersome,thus affects the classification *** eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification ***,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing *** is because of low variance in enhancing region which give poor contrast in MRI *** this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing *** trained model achieved better results for all glioma classes including the enhancing *** improved performance of our technique can be attributed to several ***,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant ***,the architecture we employ can capture the non-linearity of all classes including the enhancing ***,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

关键词： Segmentation CNN characterization brain tumor MRI

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Data-driven electrical resistance tomography for robotic large-area tactile sensing

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science China(Information sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG Huaping LIU Xiaofeng LIU Fuchun SUN School of Electrical Engineering and Automation Tianjin University of Technology Department of Computer Science and Technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

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Adaptive Control Strategy for Primary Frequency Regulation of BESS in Wind Farm 9

Adaptive Control Strategy for Primary Frequency Regulation o...

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9th Asia Conference on Power and electrical engineering, ACPEE 2024

作者： Gu, Yichao Zhuang, Kai Zhang, Jimiao Xie, Jianfeng Chongqing University of Science and Technology School of Electrical Engineering Chongqing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350309638

This paper proposes a novel adaptive synthetic inertia (SI) control scheme for the battery energy storage system (BESS) in the wind farm. The proposed approach dynamically adjusts the amplitude of the direct current (DC) of the voltage source converter (VSC) in response to grid frequency disturbances. The unique feature of the proposed control is its ability to emulate droop and inertia in real time by adjusting parameter values according to the rate of change of frequency (RoCoF) and frequency deviation. Within the SI control, a first-order low-pass element is incorporated into the virtual inertia control to augment the system inertia. Its advantages and effectiveness are rigorously demonstrated on a modified IEEE benchmark system under load changes and wind speed fluctuations. © 2024 IEEE.

关键词： Battery storage

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Unveiling the Power of Sparse Neural Networks for Feature Selection 27

Unveiling the Power of Sparse Neural Networks for Feature Se...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Atashgahi, Zahra Liu, Tennison Pechenizkiy, Mykola Veldhuis, Raymond Mocanu, Decebal Constantin van der Schaar, Mihaela Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Luxembourg Luxembourg

ISBN: (纸本)9781643685489

Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabilities while drastically reducing computational overheads. Despite these advancements, several critical aspects remain insufficiently explored for feature selection. Questions persist regarding the choice of the DST algorithm for network training, the choice of metric for ranking features/neurons, and the comparative performance of these methods across diverse datasets when compared to dense networks. This paper addresses these gaps by presenting a comprehensive systematic analysis of feature selection with sparse neural networks. Moreover, we introduce a novel metric considering sparse neural network characteristics, which is designed to quantify feature importance within the context of SNNs. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than 50% memory and 55% FLOPs reduction compared to the dense networks, while outperforming them in terms of the quality of the selected features. © 2024 The Authors.

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CYBERSECURITY ASSESSMENT AND CERTIFICATION OF CRITICAL INFRASTRUCTURES

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: electrical engineering and computer science 2024年第4期86卷 151-166页

作者： Bogdan, Ioana Corina Simion, Emil Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers "Transilvania" University of Brasov & CiTi Brasov Romania Department of Mathematical Methods and Models National University of Science and Technology POLITEHNICA Bucharest & CiTi Bucharest Romania

Cybersecurity is a field that unifies concepts from various fundamental areas: mathematics, physics, computer science, electronics, sociology, and management. This field is challenging for government authorities, academic institutions, and private entities alike. By pooling the resources of these three actors, we can build secure and resilient systems. In this context, several challenges emerge, such as the exchange of knowledge and expertise, collaborative research projects, the organization of internships and scholarship programs, the exchange of threat intelligence, training, and awareness, and finally, the standardization and regulatory component. The evolving nature of cyber threats and the growing sophistication of attackers necessitate robust and adaptable security frameworks. A "defense-in-depth security model" is mandatory in this context as it offers multiple layers of protection, ensuring that if one defense mechanism fails, others remain in place to prevent breaches. In this paper, we propose a defense-in-depth security model and highlight how key points within the model can be interconnected. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Critical infrastructures

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Vision-Based Navigation for a Small-Sized Drone: A Simple AI Strategy at IMAV'22

Vision-Based Navigation for a Small-Sized Drone: A Simple AI...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Farooq, Adil Nain Zukhraf, Syeda Zillay Shafi, Sadman Kamal, Mohsin Chumuang, Narumol University Of Cyprus Department Of Electrical And Computer Engineering Cyprus Penn State University Department Of Electrical Engineering United States National University Of Sciences And Technology School Of Electrical Engineering And Computer Science Pakistan Muban Chombueng Rajabhat University Department Of Digital Media Technology Thailand

ISBN: (纸本)9798350370096

Autonomous drone racing competitions serve as a testing ground for enhancing the perceptual, planning, and control aspects of micro unmanned aerial vehicles (MAVs). This study thoroughly outlines the strategy, methodology, and technical development of a lightweight collision avoidance module for small drones, employed during our participation in the IMAV 2022 Nanocopter AI Challenge held at TU Delft, The Nether-lands. Tailored for nano-sized drones, this competition presented challenges related to agile maneuvers, obstacle avoidance, and navigation in a dynamic indoor arena. We provide detailed insights into our obstacle avoidance algorithm, employing an AI computer vision approach and showcasing its robust performance in dynamic environments. The accuracy of the proposed strategy was verified through simulation testing, highlighting the effectiveness of our approach in a simulated Webots environment across diverse parameter settings. The shared insights aim to contribute significantly to the ongoing evolution of autonomous drone navigation. Moreover, the simulation of our proposed strategy can be seen on the YouTube video link: https://***/HVZPAfyf57M. © 2024 IEEE.

关键词： Drones

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer science 2025年第2期6卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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Course construction based on FD-QM standards and analysis and mining of course data 24

Course construction based on FD-QM standards and analysis an...

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2024 International Conference on Intelligent Education and computer Technology, IECT 2024

作者： Lv, Cheng Department of Computer Science School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798400709920

In response to the shortcomings of the SPOC course "Introduction to Computational Thinking"at Beijing University of Civil engineering and Architecture, the teaching team has transformed and improved the course design and practice based on the existing High quality undergraduate courses in Beijing universities and Beijing Curriculum Ideological and Political Demonstration Course. We aim to maintain consistency and linkage in learning objectives, learning assessments, teaching materials, learning activities, and course technology under the principle of consistent construction. Learning objectives are clearer and more measurable;learning assessment is more scientific, diverse, and reliable;Curriculum resources can meet the needs of students at different levels and majors;learning activities can fully mobilize students' subjective initiative;Curriculum technology helps to achieve learning objectives, and can reflect the characteristics of the discipline, with universality and progressiveness. Through the construction and practice of the FD-QM standard, the course team has established the principles of course standardization, clarified the direction of course material construction, drawn a knowledge graph of course knowledge points, accurately grasped the degree of learning objectives, upgraded the "characteristic"teaching resource library, enhanced student interest, and integrated ideological and political education into the curriculum, ultimately achieving a comprehensive improvement in the quality of course teaching. © 2024 ACM.

关键词： Knowledge graph

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Exploratory Data Analysis of Walmart Outlets Sales using Data Analytics Techniques

Exploratory Data Analysis of Walmart Outlets Sales using Dat...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Ramasami, Meaga Varsha Thangaraj, Rajasekaran Manoj Kumar, S. Eswaran, Sivaraman Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350310689

Sales fluctuation is the biggest problem faced by every retailer. The key to solving this issue is finding the invisible pattern of the components that have an effect on sales prediction. Nowadays retailers require a 360-degree vision of their customers to compete in the market. To achieve sales and marketing objectives, retailers must build strong promotions and offers. Retail companies employ the data from the previous year to analyse and predict sales for the upcoming year using data analysis applications. Additionally, it provides retailers with analytical insights, particularly in identifying the products liked by the customers in a particular store. In this paper, Walmart store the world's largest retailer dataset is used to figure out the business drivers and forecast which departments will be impacted by multiple parameters like average temperature, fuel price, and unemployment rate along with holidays reference and their effect on sales at stores of various locations. By visualising the analysed data, the Scala and Python API of the Spark framework is utilised to understand Walmart's marketing initiatives and data-driven plans and to acquire fresh insights into consumer behaviour. © 2023 IEEE.

关键词： Sales

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