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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

computer Modeling in engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Dynamic Occupant Adaptive Energy Management System(DOAEM) for Smart Building 10

Dynamic Occupant Adaptive Energy Management System(DOAEM) fo...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Fizza, Ghulam Kadir, Kushsairy Nasir, Haidawati Shah, Asadullah Department of Electrical and Electronic Engineering Selangort Malaysia Department of Computer Engineering Kuala Lumpur Malaysia Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350365283

The increasing global energy demands highlight the need for improved energy management systems in smart buildings. Traditional systems are inadequate in resolving the dynamic interplay between energy consumption and occupant comfort due to their inability to adapt to the dynamic nature of occupancy. The main goal of this research is to use dynamic occupancy data to optimize energy consumption. This research introduces a solution that is African Vulture Optimization Algorithm (AVOA) based Dynamic Occupant Adaptive Energy Management System. The adoption of AVOA resulted in a significant reduction in energy consumption, with a median consumption drop of 329.39 kWhr from a baseline of non-optimized 938.08 kWhr. More specifically, the implementation of AVOA showed an average improvement in energy gain to 0.9940, demonstrating the effectiveness of the system in improving energy efficiency while maintaining occupant comfort. This research proposes an integrated approach that balances energy efficiency with the well-being of its occupants, so contributing to the broader targets of sustainable urban development © 2024 IEEE.

关键词： Energy Consumption Occupants Smart Building Cost

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Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical systems 63

Reinforcement Learning-based Receding Horizon Control using ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sabouni, Ehsan Sabbir Ahmad, H.M. Giammarino, Vittorio Cassandras, Christos G. Paschalidis, Ioannis Ch. Li, Wenchao Boston University Division of Systems Engineering BostonMA02215 United States Boston University Department of Electrical and Computer Engineering The Division of Systems Engineering BostonMA02215 United States Boston University Department of Electrical and Computer Engineering Division of Systems Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced with both performance and solution feasibility significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon Control (RHC) approach leveraging Model Predictive Control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method. In order to guarantee reproducibility, our code is provided here: ***/EhsanSabouni/CDC2024_RL_adpative_MPC_CBF/tree/main © 2024 IEEE.

关键词： Adaptive control systems

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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

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Neural Computing and Applications 2025年第18期37卷 12991-13009页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Probabilistic Safety Risk Assessment near Transmission Line Structures under Fault Conditions 60

Probabilistic Safety Risk Assessment near Transmission Line ...

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60th IEEE/IAS Industrial and Commercial Power systems Technical Conference, I and CPS 2024

作者： Wang, Chenyang Liang, Xiaodong Kaur, Manpreet Manitoba Hydro Transmission & Civil Design Department WinnipegMB Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK Canada University of Manitoba Department of Electrical and Computer Engineering WinnipegMB Canada

ISBN: (纸本)9798350345308

The grounding system is an essential part of the transmission line system. In the last decade, more transmission lines have been constructed near urban areas that share the land with other public infrastructures, such as commercial lots, parks, playgrounds, walking trails, etc. This co-location raises safety concerns due to touch and step voltage hazards near transmission line structures under power line faulty conditions. In many countries, there is no national standard to tackle this concern and provide clear regulations. To overcome this issue, a probabilistic risk assessment method is utilized in this paper, which serves as a great tool to quantify this risk and make it possible to compare it with other common risks to develop acceptable criteria. A methodology is developed to determine the probability of risk, and each parameter used in the calculation is refined based on historical operational records and monitoring data. © 2024 IEEE.

关键词： Electric power transmission

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Hybrid Fuzzy Data Aggregation and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第20期48卷 95-106页

作者： Alhijaj, Asaad A. Marzook, Ali K. Hussein, Dheyaa Mezaal Alkenani, Jawad Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq University of Basrah College of Engineering Department of Electrical Engineering Basrah Iraq University of Technology Office of the President of the University Baghdad Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are essential, and the energy consumption of the sensors needs to be closely controlled. Nonetheless, there is a significant chance of redundant data because of external factors and nearby sensors. A multitude of information can be found in large datasets, some of it unnecessary and others useful. This redundancy negatively impacts performance in terms of redundant transmission and computing costs. However, data aggregation might help a network get rid of unnecessary data. In this work, we present a hybrid protocol called fuzzy data aggregation with fuzzy spider monkey optimization routing protocol (FDA-FSMORP) that represents an intelligent approach to collecting sensor data in HWSNs considering energy consumption. The results indicated that the suggested method beat in minimizing data latency our approach reduced energy consumption by 73% using energy more effectively when compared to our simulated outcomes. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sensor nodes

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Real-Time Estimation of Grid Impedance and Voltage Using an Extended Kalman Filter in Natural Frame 3

Real-Time Estimation of Grid Impedance and Voltage Using an ...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Ordono, A. Diaz-Sanahuja, C. Ortiz-Cardenas, R. Navarro-Patron, G. Guillamon-Ferrer, M. Penarrocha-Alos, I. Department of Electrical Engineering Eibar Spain Universitat Jaume I Department of Industrial Systems Engineering and Design Castelló de la Plana Spain

ISBN: (纸本)9798331540319

This paper presents a novel Extended Kalman Filter (EKF) based algorithm for real-time estimation of both grid impedance and the voltage amplitude in electrical networks. Operating directly in the natural frame, the method provides a more accurate representation of real systems subject to changing conditions, addressing limitations of traditional approaches that rely on synchronous of stationary frames. By continuously updating its estimates in real-time, the algorithm avoids the batch data processing common in many existing techniques. It also balances precision and computational complexity by choosing the appropriate variables to identify accounting for the nonlinear nature of the identification algorithm and by the addition of an internal resonator that tracks the unknown voltage. The proposed approach is particularly useful for networks with high penetration of renewable energy sources, where grid conditions can change rapidly, requiring the adaption of grid-connected converters to ensure the stability of the grid. A series of simulations have been conducted, and the initial results show good performance of the algorithm in estimating grid parameters under various dynamic conditions. © 2024 IEEE.

关键词： Extended Kalman filters

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Discrete Controller design in Grid Following Converters for Guaranteed Performance and Robustness Against Weak Grids 3

Discrete Controller Design in Grid Following Converters for ...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Navarro-Patron, G. Diaz-Sanahuja, C. Ordono, A. Guillamon-Ferrer, M. Ortiz-Cardenas, R. Penrrocha-Alos, I. Universitat Jaume I Department of Industrial Systems Engineering and Design Castelló de la Plana Spain Department of Electrical Engineering Eibar Spain

ISBN: (纸本)9798331540319

This work introduces a direct discrete-time multi-variable control strategy for grid-connected power converters using LCL filters in grid-following mode, specifically designed to manage inherent delays and prevent hidden oscillations in discrete implementation. It addresses the challenges of managing converter output current while considering grid variations and uncertainties introduced by other elements such as the Phase-Locked Loop (PLL), as well as the inherent delay in computation and Pulse Width Modulation (PWM) signal generation. The controller is defined as an structures state feedback control without the need of an observer, and it is designed to address the compromise between robustness (by minimizing the H-infinity norm of the sensitivity function), speed response (by guaranteeing a given decay rate), and noise amplification (by limiting the range of gain values). The gain computation is carried out by iteratively solving a set of Linear Matrix Inequalities problems through a Cone Complementarity Linearization. The proposed strategy is compared to traditional Proportional-Integral (PI) plus decouplers and feedforward temrs structures through comprehensive simulations, demonstrating its improved ability to handle weak grid scenarios. © 2024 IEEE.

关键词： Phase locked loops

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SEF:A Smart and Energy-Aware Forwarding Strategy for NDN-Based Internet of Healthcare

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computers, Materials & Continua 2024年第12期81卷 4625-4658页

作者： Naeem Ali Askar Adib Habbal Hassen Hamouda Abdullah Mohammad Alnajim Sheroz Khan Department of Computer Engineering Faculty of EngineeringKarabük UniversityKarabük78050Türkiye Department ofManagement Information Systems College of Business AdministrationMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based *** model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and ***,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and *** issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous *** address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based *** SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop *** approach reduces broadcast storms,optimizes overall energy consumption,and extends network *** system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed *** SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare *** demonstrated that SEF significantly enhanced NDN-based IoHT ***,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated *** forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.

关键词： Energy efficient forwarding strategy health information system internet technologies IoHT people with disabilities reinforcement learning Q-learning

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Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

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computers, Materials & Continua 2023年第1期74卷 2073-2096页

作者： Amel Ksibi Amina Salhi Ala Saleh Alluhaidan Sahar A.El-Rahman Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairoEgypt

The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as ***,personal air quality measurement remains *** this study,we investigate the use of first-person photos for the prediction of air *** main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution *** consists of two layers and four regression models,where the first layer generates meta-data fromLight Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting Regression(XGBoost)and CatBoost Regression(CatBoost),whereas the second layer computes the final prediction from the meta-data of the first layer using Extra Tree Regression(ET).The performance of the proposed AirStackNet model is validated using public Personal Air Quality Dataset(PAQD).Our experiments are evaluated using Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Coefficient of Determination(R2),Mean Squared Error(MSE),Root Mean Squared Logarithmic Error(RMSLE),and Mean Absolute Percentage Error(MAPE).Experimental Results indicate that the proposed AirStackNet model not only can effectively improve air pollution prediction performance by overcoming the Bias-Variance tradeoff,but also outperforms baseline and state of the art models.

关键词： Personal air quality prediction airstacknet ensemble learning feature extraction stacking

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