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Universal Scaling Bounds on a Quantum Heat Current

Physical Review Letters 2023年第9期131卷 090401-090401页

作者： Shunsuke Kamimura Kyo Yoshida Yasuhiro Tokura Yuichiro Matsuzaki Faculty of Pure and Applied Sciences University of Tsukuba Tsukuba 305-8571 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan

In this Letter, we derive new bounds on a heat current flowing into a quantum L-particle system coupled with a Markovian environment. By assuming that a system Hamiltonian and a system-environment interaction Hamiltonian are extensive in L, we prove that the absolute value of the heat current scales at most as Θ(L3) in a limit of large L. Furthermore, we present an example of noninteracting particles globally coupled with a thermal bath, for which this bound is saturated in terms of scaling. However, the construction of such a system requires many-body interactions induced by the environment, which may be difficult to realize with the existing technology. To consider more feasible cases, we consider a class of the system where any nondiagonal elements of the noise operator (derived from the system-environment interaction Hamiltonian) become zero in the system energy basis, if the energy difference exceeds a certain value ΔE. Then, for ΔE=Θ(L0), we derive another scaling bound Θ(L2) on the absolute value of the heat current, and the so-called superradiance belongs to a class for which this bound is saturated. Our results are useful for evaluating the best achievable performance of quantum-enhanced thermodynamic devices, including far-reaching applications such as quantum heat engines, quantum refrigerators, and quantum batteries.

关键词： Open quantum systems & decoherence Quantum control

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Robust Optimal Higher-order-observer-based Dynamic Sliding Mode Control for VTOL Unmanned Aerial Vehicles

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International Journal of Automation and computing 2021年第5期18卷 802-813页

作者： Yashar Mousavi Amin Zarei Arash Mousavi Mohsen Biari Department of Applied Science School of ComputingEngineering and Built EnvironmentGlasgow Caledonian UniversityGlasgow G4 OBAUK Department of Electrical and Computer Engineering University of Sistan and BaluchestanZahedan ***Iran Department Electrical Engineering and Applied Sciences Paradise Research CenterJahrom ***Iran Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood ***Iran

This paper investigates the precise trajectory tracking of unmanned aerial vehicles(UAV) capable of vertical take-off and landing(VTOL) subjected to external disturbances. For this reason, a robust higher-order-observer-based dynamic sliding mode controller(HOB-DSMC) is developed and optimized using the fractional-order firefly algorithm(FOFA). In the proposed scheme, the sliding surface is defined as a function of output variables, and the higher-order observer is utilized to estimate the unmeasured variables,which effectively alleviate the undesirable effects of the chattering phenomenon. A neighboring point close to the sliding surface is considered, and as the tracking error approaches this point, the second control is activated to reduce the control input. The stability analysis of the closed-loop system is studied based on Lyapunov stability theorem. For a better study of the proposed scheme, various trajectory tracking tests are provided, where accurate tracking and strong robustness can be simultaneously ensured. Comparative simulation results validate the proposed control strategy′s effectiveness and its superiorities over conventional sliding mode controller(SMC) and integral SMC approaches.

关键词： Unmanned aerial vehicle dynamic sliding mode trajectory tracking fractional firefly algorithm vertical take-off and landing system

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Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Optimizer: A Study 3

Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Opti...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sankaran, D. Abed Mohammed, Mazin Chander, Shrinithi Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Skin is a major sensory organs and the irregularity in the skin causes various issues. Skin cancer is one major medical emergency, and appropriate detection and treatment are essential. Clinical level skin cancer assessment is normally performed using the Dermoscopy Images (DI). This research aims to develop and implement the VGG-UNet scheme to extract and assess the abnormal section in DI with improved accuracy. This plan includes;(i) Image collection and resizing, (ii) Segmentation of skin lesion in DI, (iii) Comparing the segmented section with the Ground-Truth (GT), and (iv) Performance evaluation and validation. The merit of the proposed technique is confirmed by computing the necessary performance metrics. In this work, the performance of the proposed technique is verified with different optimizers (Adam/SGD) and the average pooling. The experimental work is executed on the ISIC2016 challenge dataset, and the merit of this scheme is verified with Jaccard, Dice, and Accuracy. © 2022 IEEE.

关键词： Diseases

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Kink movement on a periodic background

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Physical Review E 2025年第2期111卷 024203-024203页

作者： Tomasz Dobrowolski Jacek Gatlik Panayotis G. Kevrekidis Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchorążych 2 30-084 Cracow Poland AGH University of Krakow Faculty of Physics and Applied Computer Science 30-059 Krakow Poland Department of Mathematics and Statistics University of Massachusetts Amherst Massachusetts 01003-4515 USA

The behavior of the kink in the sine-Gordon (sG) model in the presence of periodic inhomogeneity is studied. An ansatz is proposed that allows for the construction of a reliable effective model with two degrees of freedom. Effective models featuring very good agreement with the original field-theoretic partial differential equation are constructed, including in the nonperturbative region and for relativistic velocities. The numerical solutions of the sG model describing the evolution of the kink in the presence of a barrier as well as in the case of a periodic heterogeneity under the potential additional influence of a switched bias current and/or dissipation were obtained. The results of the field equation and the effective models were favorably compared. The effect of the choice of initial conditions in the field model on the agreement of the results with the effective model is also discussed.

关键词： sine-Gordon equation

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN Computer science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

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Computers, Materials & Continua 2022年第8期72卷 3055-3071页

作者： Manar Ahmed Hamza Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Mohamed K.Nour Fahd N.Al-Wesabi Abdelwahed Motwakel Anwer Mustafa Hilal Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Modulation signal classification in communication systems can be considered a pattern recognition *** works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,*** recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation *** this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication *** proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated *** addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)***,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO *** order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried *** experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

关键词： Complex systems communication system signal processing modulation signals classification artificial intelligence deep learning parameter tuning

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On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI

On a Survival Gradient Boosting, Neural Network and Cox PH B...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Henry Musto Daniel Stamate Doina Logofatu Lahcen Ouarbya Department of Computing Goldsmiths College University of London United Kingdom Department of Computing Goldsmiths College University of London and School of Health Sciences The University of Manchester United Kingdom Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Germany

ISBN: (数字)9798350337488

ISBN: (纸本)9798350337495

In recent years, attention within the clinical prediction community has turned to the use of survival machine learning as a tool for predicting the risk of developing a disease as a function of time. The current work seeks to contribute to existing literature which demonstrates the utility of these methods when applied to a dementia prediction context. We use the Alzheimer's Disease Neuroimaging Initiative ADNI dataset and model deterioration within two distinct groups, those deemed cognitively normal and those with a formal diagnosis of Mild Cognitive Impairment. In agreement with existing literature we find that survival machine learning outperforms standard survival analysis methods such as Cox PH model, and has very good predictive ability. We propose an innovative approach to predicting dementia diagnosis risk on ADNI, which explores the use of survival neural network and survival extreme gradient boosting techniques that have hitherto seldom been applied to this context. The stability of our models was investigated within a Monte Carlo simulation framework.

关键词： Analytical models Biological system modeling Neural networks Predictive models Boosting Stability analysis Alzheimer's disease

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User Interaction Development for Radiotherapy Virtual Reality Training Application

User Interaction Development for Radiotherapy Virtual Realit...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Chan, Vei Siang Iglesias, Andres Mohamed, Farhan Salleh, Faezah M. Haron, Habibollah Jamaludin, Najwa Ayuni Galvez, Akemi Prada, Pedro J. Ruiz, Samuel You, Lihua Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia Universidad de Cantabria Dept. of Applied Mathematics and Computational Sciences Santander Spain Toho University Faculty of Pharmaceutical Sciences Funabashi Japan Media and Game Innovation Center of Excellence Universiti Teknologi Malaysia Johor Bahru Malaysia Universiti Teknologi Malaysia Faculty of Science Johor Bahru Malaysia Hospital Universitario Marqués de Valdecilla Santander Spain National Center for Computer Animation Bournemouth University Poole United Kingdom

ISBN: (纸本)9798350302684

The advancement of virtual reality (VR) technology can enrich pedagogical approaches and information communication. In the oncology radiotherapy treatment, the medical practitioners revealed two main issues: the lack of training infrastructure and patient comprehension of the treatment process. Virtual simulation can assist in solving these complexities by offering an economical virtual embodiment of the medical facility. This study aims to improve the user interface and experience of VR training applications from previous work by conducting a domain expert intervention methodology. This paper discusses the main challenges and features of user interaction design for effective VR training systems and optimises the system based on feedback from domain experts. The authors anticipate that the key factors and design considerations proposed for this VR training application can benefit the development of related training systems in many other domains. © 2023 IEEE.

关键词： Virtual reality

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Integrating Artificial Intelligence in Improving Educational System: The Mediating role of Smart Learning 2

Integrating Artificial Intelligence in Improving Educational...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Akour, Iman A. Muhammad Alshurideh, Hevron Alzoubi, Haitham M. Alshurideh, Muhammad Turki Ahmad Antouz, Yasin University of Sharjah College of Computing and Informatics Department of Information Systems Sharjah27272 United Arab Emirates The University of Jordan Faculty of English Language and Literature Department of Foreign Languages Amman11942 Jordan Skyline University College School of Business Sharjah United Arab Emirates Applied Science Private University Applied Science Research Center Amman Jordan University of Sharjah College of Business Department of Management Sharjah27272 United Arab Emirates The University of Jordan School of Business Department of Marketing Amman11942 Jordan

ISBN: (纸本)9798350335644

One of the contemporary developments in educational technology is artificial intelligence (AI) in the education. The use of AI will lead to a significant change in governance across the board for UAE institutes of learning. The potential use of AI in education includes an examination of the educational consequences, including how teachers would be enriched, how students would learn, and how fast and accurate judgements could be made in education institutions. Given this situation, AI assistance is absolutely necessary. Despite the fact that it has been around for close to 30 years, educators are still confused of how to use it pedagogically on a broader scale and how it may actually have a big impact on teaching and learning in higher education. Through an empirical review, this research aims to present an empirical analysis of the application of artificial intelligence in educational system with mediating effect of smart learning. This research carried out empirical evidence from 28 educational institutions based in UAE. The managerial and professors were approached in order to answer the online questionnaire. 195 valid responses were considered for data analysis using SmartPLS. Model assessment done on the basis of various tests, for instance, reliability, discriminant validity, PLS-SEM. The findings suggest a significant relationship between artificial intelligence and educational system, and mediating role of smart learning is predicted to be significant. Hence, the intention to use artificial intelligence with smart learning can enhance the academic learning outcomes. © 2023 IEEE.

关键词： Artificial intelligence

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Deep Learning Enabled Microarray Gene Expression Classification for Data science Applications

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Computers, Materials & Continua 2022年第11期73卷 4277-4290页

作者： Areej A.Malibari Reem M.Alshehri Fahd N.Al-Wesabi Noha Negm Mesfer Al Duhayyim Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In bioinformatics applications,examination of microarray data has received significant interest to diagnose *** gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of *** data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern *** paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics *** proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform ***,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical ***,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)*** utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification *** examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark *** extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

关键词： Bioinformatics data science microarray gene expression data classification deep learning metaheuristics

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