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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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Novel 3-dimensional virtual hepatectomy simulation combined with real-time deformation

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World Journal of Gastroenterology 2015年第34期21卷 9982-9992页

作者： Yukio Oshiro Hiroaki Yano Jun Mitani Sangtae Kim Jaejeong Kim Kiyoshi Fukunaga Nobuhiro Ohkohchi Department of Surgery Division of Gastroenterological and Hepatobiliary Surgery and Organ TransplantationFaculty of MedicineUniversity of Tsukuba Department of Computer Science Graduate School of System Information EngineeringUniversity of Tsukuba School of Informatics College of Media Arts Science and TechnologyFaculty of Library Information and Media ScienceUniversity of Tsukuba

AIM:To develop a novel 3-dimensional(3D) virtual hepatectomy simulation software,Liversim,to visualize the real-time deformation of the ***:We developed a novel real-time virtual hepatectomy simulation software program called Liversim. The software provides 4 basic functions:viewing 3D models from arbitrary directions,changing the colors and opacities of the models,deforming the models based on user interaction,and incising the liver parenchyma and intrahepatic vessels based on user operations. From April 2010 through 2013,99 patients underwent virtual hepatectomies that used the conventional software program SYNAPSE VINCENT preoperatively. Between April 2012 and October 2013,11 patients received virtual hepatectomies using the novel software program Liversim; these hepatectomies were performed both preoperatively and at the same that the actual hepatectomy was performed in an operating room. The perioperative outcomes were analyzed between the patients for whom SYNAPSE VINCENT was used and those for whom Liversim wasused. Furthermore,medical students and surgical residents were asked to complete questionnaires regarding the new ***:There were no obvious discrepancies(i.e.,the emergence of branches in the portal vein or hepatic vein or the depth and direction of the resection line) between our simulation and the actual surgery during the resection process. The median operating time was 304 min(range,110 to 846) in the VINCENT group and 397 min(range,232 to 497) in the Liversim group(P = 0.30). The median amount of intraoperative bleeding was 510 m L(range,18 to 5120) in the VINCENT group and 470 m L(range,130 to 1600) in the Liversim group(P = 0.44). The median postoperative stay was 12 d(range,6 to 100) in the VINCENT group and 13 d(range,9 to 21) in the Liversim group(P = 0.36). There were no significant differences in the preoperative outcomes between the two groups. Liversim was not found to be clinically inferior to SYNAPSE VINCENT. Both

关键词： Liver Surgery Simulation Virtual hepatectomy Real-

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A new watershed algorithm for protein spot detection in 2DGE images using the notion of fuzzy relations

A new watershed algorithm for protein spot detection in 2DGE...

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2010 2nd International Conference on computer Technology and Development, ICCTD 2010

作者： Rashwan, Shaheera Faheem, Talaat Sarhan, Amany Youssef, Bayumy A.B. Informatics Research Institute Mubarak City for Science and Technology Borg ElArab Alexandria Egypt Computer Science and Automatic Control Department Faculty of Engineering Tanta University Tanta Egypt

ISBN: (纸本)9781424488438

An important issue in the analysis of two-dimensional electrophoresis images is the detection and quantification of protein spots. The main challenges in the segmentation of 2DGE images are to separate overlapping protein spots correctly and to find the abundance of weak protein spots. To enable comparison of protein patterns between different samples, it is necessary to match the patterns so that homologous spots are identified. In this paper we describe a new robust technique to segment and model the different spots present in the gels. The watershed segmentation algorithm is modified to handle the problem of over segmentation by initially partitioning the image to mosaic regions using the composition of fuzzy relations. The experimental results showed the effectiveness of the proposed algorithm to overcome the over segmentation problem associated with the available algorithms. © 2010 IEEE.

关键词： Electrophoresis

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MPPT techniques application and comparison for photovoltaic panels 23

MPPT techniques application and comparison for photovoltaic ...

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23rd International Conference on control systems and computer science Technologies, CSCS 2021

作者： Dobrea, Marius-Alexandru Arghira, Nicoleta Vasluianu, Mihaela Neculoiu, Giorgian Moldoveanu, Ana-Maria Cristiana University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering Faculty of Hidrotechnics Bucharest Romania

ISBN: (纸本)9781665439398

Photovoltaic (PV) panel modelling and control is very important in renewable energy systems. Due to it variability, PV panel generation power should be maximized for the given climate conditions. This paper considers three Maximum Power Point Tracking (MPPT) algorithms: Perturb Observe, Incremental Conductance and Fuzzy Logic. After a clear description for each algorithm, modelling, testing and validation is conducted on real experimental data for a 250W PV panel. Over one day period, a comparison between the 3 control MPPT algorithms show that Perturb Observe gives the best results, close to Incremental Conductance algorithm application. © 2021 IEEE.

关键词： Fuzzy logic

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Defect engineering in β-Ga2O3: Hybrid functional study on the formation and stability of copper substitutional and complex defects

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Materials Chemistry and Physics 2025年 343卷

作者： Nguyen, Duy Khanh Nhan, Duong Trong Dam, Dang Phuc Vo, Khuong Dien Huy, Huynh Anh Tien, Nguyen Thanh Ho, Quoc Duy Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam College of Natural Sciences School of Education Can Tho University Can Tho City Viet Nam FPT University Can Tho Campus Can Tho Viet Nam Faculty of Physics School of Education Can Tho University Can Tho City Viet Nam Department of Mathematics and Physics Universitetet i Stavanger Stavanger Norway

The formation of single substitutional copper (Cu) at the gallium (Ga) site and Cu complex defects in monoclinic gallium oxide (β-Ga2O3) has been studied using an optimized hybrid functional approach. Our calculations show that substitutional Cu is more favorable at the octahedral Ga (CuGa2) site compared to the tetrahedral Ga (CuGa1) site. Furthermore, substitutional of Cu at gallium site (CuGa) tends to form complex defects with oxygen vacancies (CuGa-VO) when both defects are present in the sample. The relative concentrations of single substitutional CuGa and CuGa-VO complexes in β-Ga2O3 depend significantly on the crystal growth conditions. This study highlights the favorability of the CuGa2 complex defect with oxygen 1 vacancy (VO1) over other possible CuGa-VO configurations. Based on our results, we assign the CuGa2 in the −1 charge state and CuGa2+VO1 in the +1 charge state to the Cu2+(A) and Cu2+(B) defect centers, respectively. Importantly, the study demonstrates that oxygen vacancies not only compensate acceptor dopants but also can form complex defects with Cu, acting as donor defects. These findings underscore the importance of carefully controlling oxygen vacancies and crystal growth methods when doping β-Ga2O3 with metals to achieve p-type conductivity, which is essential for optoelectronic applications. © 2025 The Authors

关键词： Gallium compounds

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Ensemble Model for Detection and Classification of Brain Tumors 4

Ensemble Model for Detection and Classification of Brain Tum...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Bhagyalaxmi, K. Dwarakanath, B. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Ramapuram India Srm Institute of Science and Technology Department of Information Technology Tamilnadu Ramapuram India

ISBN: (纸本)9798331529635

Brain tumors, characterized by abnormal cell growth within the brain, present significant challenges for early detection and accurate classification due to their complex and heterogeneous nature. Manual evaluation of MRI scans, though critical, is often laborintensive and prone to errors, highlighting the need for advanced, automated diagnostic methods. Convolutional Neural Networks (CNNs) have demonstrated strong potential in this domain by effectively learning hierarchical features from raw medical images to detect and classify brain tumors. However, single CNN models often encounter difficulties in handling the variability in tumor appearance, such as differences in shape, size, and intensity. To address these challenges, this research proposes an ensemble model that integrates pretrained CNN architectures-ResNet50, Inception, and Xception. The ensemble technique strengthens the adaptability and accuracy of brain tumor identification and categorization by incorporating the advantages of these distinct models. The proposed model was trained and evaluated using a large dataset of 5,316 MRI images, representing a range of brain tumor types, including gliomas, pituitary, meningiomas, and cases no tumor. With a high classification accuracy of 99.39%, our ensemble model proved to be useful in enhancing the reliability of diagnoses and assisting surgeons in making decisions on the treatment of brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Energy-independent solution using a medium-power wind turbine to ensure the irrigation deficit in an isolated agricultural areal 24

Energy-independent solution using a medium-power wind turbin...

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24th International Conference on control systems and computer science, CSCS 2023

作者： Fudulu, Cosmin-Florin Fagarasan, Ioana Boicu, Mihaela Gabriela Vasluianu, Mihaela Dobrea, Marius - Alexandru Neculoiu, Giorgian University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Romania

ISBN: (纸本)9798350313390

Smart agricultural systems require irrigation systems powered by renewable energy sources that are also adaptable for isolated areas without connection possibility to the electricity network or the water network. For such situations, in this paper, a solution is proposed using a wind turbine and a submersible pump to feed a water basin, subsequently irrigation is done by free fall. The developed solution is designed and simulated using a model of a medium-power wind turbine, and a battery storage element with the aim of providing the necessary electricity for irrigation of an agricultural area in Buzau, where there is a water deficit. © 2023 IEEE.

关键词： Energy efficiency

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Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

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computer systems science & engineering 2023年第7期46卷 765-781页

作者： Ahmed M.Elshewey Mahmoud Y.Shams Zahraa Tarek Mohamed Megahed El-Sayed M.El-kenawy Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentMansoura University35561Egypt Faculty of Physical Education Track and Field Competitions DepartmentArish UniversityEgypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Food choice motives(i.e.,mood,health,natural content,convenience,sensory appeal,price,familiarities,ethical concerns,and weight control)have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the *** from several domains have presented several models addressing issues influencing food choice over the ***,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making *** this paper,four Deep Learning(DL)models and one Machine Learning(ML)model are utilized to predict the weight in pounds based on food *** Long Short-Term Memory(LSTM)model,stacked-LSTM model,Conventional Neural Network(CNN)model,and CNN-LSTM model are the used deep learning *** the applied ML model is the K-Nearest Neighbor(KNN)*** efficiency of the proposed model was determined based on the error rate obtained from the experimental *** findings indicated that Mean Absolute Error(MAE)is 0.0087,the Mean Square Error(MSE)is 0.00011,the Median Absolute Error(MedAE)is 0.006,the Root Mean Square Error(RMSE)is 0.011,and the Mean Absolute Percentage Error(MAPE)is ***,the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM,CNN,CNN-LSTM,and KNN regressor.

关键词： Weight prediction machine learning deep learning LSTM CNN KNN

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Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

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computers, Materials & Continua 2022年第4期71卷 1567-1580页

作者： E.Ahila Devi K.C.Ramya K.Sathesh Kumar Sultan Ahmad Seifedine Kadry Hyung Ju Park Byeong-Gwon Kang Department of Instrumentation and Control Engineering St.Joseph’s College of EngineeringChennai600119India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and *** nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery *** planning in MANET is a multi-hop communication process due to the restricted transmission range of the *** aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy *** few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design *** this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for *** EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR *** fitness value of the created solutions is determined next to pass the solutions to the objective *** MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy *** ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take *** resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared *** resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.

关键词： MANET routing lar protocol energy efficiency mrfo algorithm metaheuristics

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Balance control of a Biped Robot on a Rotating Platform Based on Efficient Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 938-951页

作者： Ao Xi Thushal Wijekoon Mudiyanselage Dacheng Tao Chao Chen the Department of Mechanical and Aerospace Engineering Monash UniversityClaytonVIC 3800Australia IEEE Monash University ClaytonVIC 3800Australia the UBTECH Sydney Artificial Intelligence Centre and the School of Computer Science the Faculty of Engineeringthe University of SydneyNSW 2008Australia

In this work,we combined the model based reinforcement learning(MBRL)and model free reinforcement learning(MFRL)to stabilize a biped robot(NAO robot)on a rotating platform,where the angular velocity of the platform is unknown for the proposed learning algorithm and treated as the external *** Gaussian processes normally require a large number of training data points to deal with the discontinuity of the estimated *** some improved method such as probabilistic inference for learning control(PILCO)does not require an explicit global model as the actions are obtained by directly searching the policy space,the overfitting and lack of model complexity may still result in a large deviation between the prediction and the real ***,none of these approaches consider the data error and measurement noise during the training process and test process,*** propose a hierarchical Gaussian processes(GP)models,containing two layers of independent GPs,where the physically continuous probability transition model of the robot is *** to the physically continuous estimation,the algorithm overcomes the overfitting problem with a guaranteed model complexity,and the number of training data is also *** policy for any given initial state is generated automatically by minimizing the expected cost according to the predefined cost function and the obtained probability distribution of the ***,a novel Q(λ)based MFRL method scheme is employed to improve the *** results show that the proposed RL algorithm is able to balance NAO robot on a rotating platform,and it is capable of adapting to the platform with varying angular velocity.

关键词： Biped robot Gaussian processes(GP) reinforcement learning temporal difference

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