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Boosting the generation rate of squeezed single-photon states by generalized photon subtraction

Physical Review A 2024年第3期110卷 033717页

作者： Hiroko Tomoda Akihiro Machinaga Kan Takase Jun Harada Takahiro Kashiwazaki Takeshi Umeki Shigehito Miki Fumihiro China Masahiro Yabuno Hirotaka Terai Daichi Okuno Shuntaro Takeda Department of Applied Physics School of Engineering Optical Quantum Computing Research Team NTT Device Technology Labs Advanced ICT Research Institute

In optical quantum information processing with continuous variables, optical non-Gaussian quantum states are essential for universal and fault-tolerant quantum computation. Experimentally, their most typical generation method is photon subtraction (PS), where single-photon detection by an on/off detector probabilistically heralds the generation of squeezed single-photon states. In PS, however, trying to avoid unwanted multiphoton detection inevitably limits the generation rate, hindering the application of squeezed single-photon states. Here, we theoretically show that generalized photon subtraction (GPS), a simple extension of PS, can improve the generation rate while maintaining the quality of the generated states. Furthermore, we experimentally demonstrate the generation rate improvement for 2-dB- and 4-dB-squeezed single-photon states compared to PS, by more than one order of magnitude, particularly for the case of 2 dB. Our results will accelerate the application of squeezed single-photon states to more advanced quantum information protocols.

关键词： Quantum information processing Quantum optics

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

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Computers, Materials & Continua 2023年第3期74卷 5641-5661页

作者： Afia Zafar Muhammad Aamir Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaMalaysia School of Electronics Computing and MathematicsUniversity of DerbyUK Department of Computer Science National University of TechnologyIslamabadPakistan Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Systems Faculty of Computer and Information SciencesIslamic University of MadinahMadinah42351Saudi Arabia

In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

关键词： Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization

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Streamlining Task Planning Systems for Improved Enactment in Contemporary computing Surroundings

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SN Computer Science 2024年第8期5卷 993页

作者： Menon, Sindhu Addula, Santosh Reddy Parkavi, A. Subbalakshmi, Ch. Dhandayuthapani, V. Bala Pokkuluri, Kiran Sree Soni, Anita School of Computing and Information Technology REVA University Karnataka Bangalore India Department of Information Technology University of the Cumberlands Williamsburg KY United States Dept of CSE M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Guru Nanak Institution Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Oman Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India CSE Department IES University Bhopal Madhya Pradesh Bhopal India

Task planning algorithms are essential for maximizing efficiency and enhancing performance in modern computing systems, given the escalating demand for computational resources. This paper delves into the effectiveness of various scheduling algorithms across different computing environments, characterized by their unique workload dynamics and resource limitations. This method integrates adaptive algorithms and machine learning to dynamically optimize task planning, enhancing performance and efficiency in cloud, IoT, and distributed computing environments. These algorithms are crucial for optimizing key factors such as task completion times, job prioritization, and resource allocation while also addressing quality of service (QoS), cost, reliability, and specific resource needs. One significant challenge is that many task scheduling approaches do not account for the potential failure of tasks or resources. While some strategies effectively reduce overall completion time (makespan), they can result in severe workload imbalances. To address these issues, our study proposes a new approach that harnesses the processing capabilities of grid systems, thus boosting application performance and throughput. Our algorithm particularly focuses on balancing workload and accelerating scheduling operations, even in the face of grid node failures, by incorporating QoS metrics. This allows for a more robust and adaptable scheduling solution. Comparative analysis with existing methods demonstrates that our algorithm not only improves resource utilization but also significantly diminishes flow time and makespan, confirming its efficacy. Through this research, we contribute to the evolving field of autonomous task scheduling, presenting a solution that responds dynamically to changing environmental conditions and workload demands. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CPU Flow time Makespan MSHA QoS

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Exploring the Usability of Quantum Machine Learning for EEG Signal Classification 13th

Exploring the Usability of Quantum Machine Learning for E...

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13th International Advanced computing Conference, IACC 2023

作者： Singh, Devansh Kanathey, Yashasvi Waykole, Yoginii Mishra, Rohit Kumar Walambe, Rahee Aqeel, Khan Hassan Kotecha, Ketan Symbiosis Institute of Technology Symbiosis International University Pune India Centre for Development of Advanced Computing Delhi India SIU Pune India Department of Applied Artificial Intelligence and Robotics Aston University Birmingham United Kingdom

ISBN: (纸本)9783031566998

The classification of Electroencephalogram (EEG) signals into distinct frequency bands is a critical task in understanding brain function and diagnosing neurological disorders. The information obtained from frequency-specific classification has multiple applications, such as frequency-based wheelchair control, frequency-based 36-stroke brain operated keyboard for paralysed patients etc. In this work, a method based on machine learning to develop the frequency-based classification of EEG signals is proposed. The performance of Classical Machine Learning (CML) algorithms and Quantum Machine Learning (QML) techniques for the classification of EEG signals across four frequency bands are investigated. The primary objective is to evaluate the performance of QML models against traditional CML models in terms of computational efficiency, time efficiency and accuracy and uncover potential benefits offered by quantum computing for a particular task of classifying EEG signals. The goal is to assess the advantages of using quantum algorithms for classifying EEG signals. This includes improving accuracy and enhancing efficiency. These findings add to the existing knowledge about how quantum machine learning can benefit neuroscience in terms of enhancing methods that rely on EEG data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Asthma Disease Risk Prediction Using Machine Learning Techniques

Asthma Disease Risk Prediction Using Machine Learning Techni...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Selvaraj, P.A. Kumar, Kamdev Aadarsh, V. Mariya Arockiya Akash, A. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences IT Department Oman

ISBN: (纸本)9798350348217

The signs of asthma, a long-term inflammatory condition of the airways, include wheezing, throat tightness, coughing, and breathing difficulties. The attack of an asthma, which can be fatal, is the fast worsening of these symptoms. Another severe, irreversible airflow restriction in the lungs is caused by respiratory COPD, which encompasses emphysema and chronic bronchitis. In this project, a machine learning-based algorithm is for predicting asthma risk is presented (ML). PEFR, which are widely used external tools like peak flow meters and recognized asthma risk predictors, are frequently monitored. This study shows a relationship between the ambient particle matter(PM) and the weather outdoors. According to the best peak flow value each person was able to acquire, the results are divided into two groups: Safe and Risk circumstances. The link between indoor PM and weather data is mapped to the found values using a convolutional neural network (CNN) architecture. The suggested method's root mean square and mean absolute error accuracy metrics are contrasted with those of current deep neural network (DNN)-based methods. Additionally, the accuracy of the classification methods KNN and SVM are carried out. The new data set's asthma category may be predicted more accurately thanks to the application of SVM, KNN, and CNN classification. Python 3.7 is the coding language employed. © 2023 IEEE.

关键词： Support vector machines

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Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) Architecture for Lung Cancer Subtype Classification

Convolutional Neural Network-Long Short-Term Memory (CNN-LST...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： S. Ahamed Nishath Suresh Rasappan Francis Saviour Devaraj Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Mathematics Department University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Lung cancer is a leading cause of cancer-related deaths worldwide. Accurate classification of lung cancer subtypes is crucial for effective treatment. This study proposes a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for classifying lung cancer subtypes: Adenocarcinoma, Squamous Cell Carcinoma, and Lymph Node involvement. Our model achieves 95.38% on a dataset LC105K of 105,000 images, outperforming traditional methods. The CNN-LSTM architecture extracts features from images and models temporal dependencies, improving classification accuracy.

关键词： Technological innovation Accuracy Squamous cell carcinoma Memory architecture Lung cancer Feature extraction Convolutional neural networks Reliability Lymph nodes Long short term memory

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Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

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4th International Conference on Data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Multi-Module Spatial Semantic Network with Gradient Boosting for Efficient Crop Classification

A Multi-Module Spatial Semantic Network with Gradient Boosti...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Husain, Saif O. Meghana, A. Soujanya, Rajireddy Sunil, G. Reddy Muvva, Vijaya Bhaskar The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman

ISBN: (纸本)9798350375442

For the past few years, accurate crop classification is critical for efficient agricultural management and monitoring. While traditional classification techniques have leveraged spectral indices, they often struggle to fully capture the complex and diverse multispectral features present in agricultural landscapes. This challenge is particularly evident when differentiating crops with similar spectral signatures, leading to misclassification and reduced accuracy in traditional models. To address this problem, a hybrid approach that combines a deep learning model namely Multi-module Spatial Semantic Network (MSSNet) with the Gradient Boosting Classifier (GBC) is proposed. The dataset utilized in this research is sentinel-2, where the median filter is used to remove noise at the preprocessing stage. Then, MSSNet architecture is applied in feature extraction where pre-trained residual network (ResNet50), embedded receptive field module (RFB) and skip connection are incorporated to extract multi-spectral and spatial features. Finally, the extracted features are fed into a GBC, which is suitable for the robust performance in pixel mapping. This MSSNet+GBC combination leverages the strengths and offers improved accuracy in distinguishing between crop types and conditions. The experimental results demonstrate that MSSNet+GBC outperforms conventional methods such as 3D-Convolutional Neural Network (CNN) with higher accuracy of 98.67% using a sentinel-2 dataset for precise crop classification. © 2024 IEEE.

关键词： Median filters

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Location Anonymous Query Algorithm based on Road Networks

Location Anonymous Query Algorithm based on Road Networks

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Haohua, Qing Ibrahim, Roliana Nies, Hui Wen Universiti Teknologi Malaysia Faculty of Computing Department of Applied Computing and Artificial Intelligence Johor81310 Malaysia Guangzhou College of Technology and Business Guangdong Guangzhou510850 China

ISBN: (纸本)9781665489621

In this paper, we study the location anonymous query algorithm based on the Voronoi graph division of the road networks. The Voronoi diagram spatial analysis model has unique functions and application potential in the description of the location problems, the spatial partitioning of the landmarks based on the Voronoi diagram, on the one hand, it achieves the division of the spatial extent covered by the POI under the principle of shortest distance, and on the other hand, it also deals with the problem of the spatial partitioning. Hence, we propose the novel ideas of improving the division performance and also the query algorithm enhancement. Through the simulation, we select the Foshan city data as the target to evaluate the proposed model. © 2022 IEEE.

关键词： Location

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