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SSRN 2023年

作者： Song, Jiayin Lu, Chao Zhao, Pengpeng Song, Wenlong Fan, Yiming Huang, Qiqi Jiang, Zhuoyuan Liao, Ting Wang, Chuangqi Zhu, Yuhan Zhou, Jiancai College of Computer and Control Engineering Northeast Forestry University He Xing Road Heilongjiang Harbin150040 China Shanghai Electric Power Company State Grid Yuan Shen Road Shanghai200122 China

This paper proposes an Enhanced Slime Mould algorithm (ESMA) for solving the multi-objective optimal scheduling problem in microgrids (MG). The ESMA combines the Slime Mould Algorithm (SMA) with the Equilibrium Optimizer (EO). The MG optimal scheduling problem is formulated as a nonlinearly constrained optimization problem, considering three objective functions: management cost, pollution treatment cost, and cut-off load cost, along with multiple constraints. The performance of the ESMA algorithm is verified by testing on 12 benchmark test functions and then compared with the Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and SMA algorithms under identical conditions. The results demonstrate that the SMA algorithm exhibits an advantage in achieving the optimal value. Subsequently, the ESMA algorithm is applied to the off/on-grid dispatching scheme, where the weights of the multiple objective functions in the optimization scheme are determined using the ESMA-fuzzy AHP algorithm. Simulation results illustrate that the ESMA algorithm effectively fulfills the load demand within the MG while minimizing the operational cost through appropriate power output deployment and flexible load adjustment. This ensures the economic viability and power supply reliability of the MG operation. © 2023, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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A new Technique in Protein Structure Quantitative Identification 9th

A new Technique in Protein Structure Quantitative Identifica...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Guo, Mengjiao Cao, Haobin He, Jing Ji, Tengfei Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne3122 Australia Department of Mechanical Engineering University College London LondonWC1E 6BT United Kingdom Department of Neuroscience University of Oxford OxfordOX39DU United Kingdom State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia Medica Peking Union Medical College Chinese Academy of Medical Sciences Beijing100050 China

Graph distance is also named approximate graph isomorphism, error-tolerant graph matching, it is a measure of similarity (or dissimilarity) between two graphs. The graph distance function is an elusive question remaining a pressing problem in a wide range of fields. In general, graphs are usually enriched with node and edge attributes, namely, heterogeneous graphs, our focus is encapsulating node and edge identities in a graph. In addition, an adequate representation of the graph-adjacency matrix can capture the connectivity of the graph structure, while an adjacency matrix bears high-order combinatorial nature. Solving these challenges in the effective analysis of these graphs is of utmost importance. In this paper, structural and semantic features are both preserved by graphs, so the analysis of row sum and eigenspectra for vertex and edge adjacency matrix has captured the affinity interactions within graphs locally and globally. Therefore, we estimate the geometrical and semantic dissimilarities/distances between graphs extensively and systemically. Several illustrative chemical compound cases implement in practical scenarios, thus, the theory could be easily understood. Our method defines a quantitative distance (or dissimilarity) measure for comparing graphs, it has a good measure of expressiveness and a suited polynomial computation complexity, which paves the way for graph analysis. © 2022 The Authors. Published by Elsevier B.V.

关键词： Graphic methods

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Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks

arXiv

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arXiv 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Wu, Qihui Zhu, Qiuming Niyato, Dusit Han, Zhu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China State Key Laboratory of ISN Xidian University Xian710071 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering the University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehicles in multilayer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Network function virtualization

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application

Investigating Principle Component Analysis Impact on the Per...

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Information Technology (ACIT)

作者： Ahmed BaniMustafa Sattam Almatarneh Olla Bulkrock Raed Alazaidah Hossam Almahasneh Ghassan Samara Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE Data Science & Artificial Intelligence Dept. Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion.

关键词： Training Logistic regression Cells (biology) Nearest neighbor methods Vectors Numerical models Classification algorithms Random forests Principal component analysis Biomedical imaging

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Text Generation of Speech Imagery Based on an Enhanced CTA-BiLSTM Model Utilizing EEG Signals

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IEEE Transactions on Consumer Electronics 2025年

作者： Pan, Hongguang Chu, Xin Miao, Rui Wang, Mei Wang, Yiran Li, Zhuoyi College of Electrical and Control Engineering Xi’an University of Science and Technology Xi’an710054 China Xi’an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security Xi’an710054 China LTD Company of Shenzhen HQVT Technology Co Shenzhen518100 China College of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China Northwestern Polytechnic University School of Automation Xi’an710072 China

Recent studies have demonstrated the potential application of speech imagery neural signals in brain–computer interface (BCI) technology. Text generation based on speech imagery offers a natural communication method for individuals with speech disabilities. However, the limitations in imagined content and the immaturity of text generation technology currently constitute an obstacle to its applications. Therefore, this study proposes an enhanced CTA-BiLSTM model for efficient text generation utilizing speech imagery electroencephalography (EEG) signals, significantly enhancing the accuracy and fluency of text generation. Firstly, distinct from the prevailing imagination of characters and words, this study has assembled a sentence-level EEG dataset from ten subjects to facilitate communication. Subsequently, addressing the temporal dynamics characteristics and sequence dependencies of sentence signals, we employ dynamic time warping (DTW) and hidden Markov models (HMM) for accurate temporal alignment and signal annotation to generate fine-grained sentence labels. Finally, the proposed CTA-BiLSTM model leverages channel-time attention mechanism to dynamically adjust weights across channels and time, emphasizing critical features. Concurrently, the bidirectional long short-term memory (BiLSTM) network captures and utilizes long-term dependencies in the EEG signals, thereby enhancing the accuracy of the model in decoding complex temporal patterns. The experimental results demonstrate that the average sentence decoding accuracy can reach 67.50% on the self-built dataset, realizing a better evaluation accuracy and validating its potential for application. © 1975-2011 IEEE.

关键词： Hidden Markov models

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Eldo-care: EEG with Kinect sensor based telehealthcare for the disabled and the elderly

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Neuroscience Informatics 2023年第2期3卷 100130-100130页

作者： Das, Sima Adhikary, Arpan Laghari, Asif Ali Mitra, Solanki Department of Computer Science and Engineering Bengal College of Engineering and Technology West Bengal Durgapur India Software College Shenyang Normal University Shenyang China School of Computing Science University of Glasgow 18 Lilybank Gardens Glasgow G12 8RZ United Kingdom

Telehealthcare systems are nowadays becoming a massive daily helping kit for elderly and disabled people. By using the Kinect sensors, remote monitoring has become easy. Also, the sensors' data are useful for the further improvement of the device. In this paper, we have discussed our newly developed “Eldo-care” system. This system is designed for the assessment and management of diverse neurological illnesses. The telemedical system is developed to monitor the psycho-neurological condition. People with disabilities and the elderly frequently experience access issues to essential services. Researchers today are concentrating on rehabilitative technologies based on human-computer interfaces that are closer to social-emotional intelligence. The goal of the study is to help old and disabled persons with cognitive rehabilitation using machine learning techniques. Human brain activity is observed using electroencephalograms, while user movement is tracked using Kinect sensors. Chebyshev filter is used for feature extraction and noise reduction. Utilizing the autoencoder technique, categorization is carried out by a Convolutional neural network with an accuracy of 95% and higher based on transfer learning. A better quality of life for older and disabled persons will be attained through the application of the suggested system in real time. The proposed device is attached to the subject under monitoring. © 2023 The Author(s)

关键词： Deep learning Electroencephalogram Home monitoring Kinect sensor Telehealth care

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Data security and privacy computing in artificial intelligence

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Journal of Information and Intelligence 2024年第2期2卷 99-101页

作者： Dengguo Feng Hui Li Rongxing Lu Zheli Liu Jianbing Ni Hui Zhu Institute of Software Academy of SciencesBeijing 100190China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Faculty of Computer Science University of New BrunswickFredericton E3B 5A3Canada College of Computer Science Nankai UniversityTianjin 300071China Department of Electrical and Computer Engineering Queen’s UniversityKingston K7L 3N6Canada

Artificial intelligence(AI)is a field of computer science dedicated to creating systems and algorithms that can perform tasks typically requiring human intelligence,such as learning,problem-solving,language understanding,and decision-making,contributing to a wide array of applications across diverse *** development of AI,such as machine learning and deep learning,has revolutionized data processing and *** transformation is rapidly changing human life and has allowed for many practical AI based applications,including biometric recognition,text/sentimental analysis,and attack detection in the fields of health care,finance,autonomous vehicles,personalized ***,the potential benefits of AI are hindered by issues,such as insecurity and privacy violations in data processing and communication.

关键词： such has finance

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3D CT Liver Images Lesion Extraction and Classification for Using 3D-CNN with GLRLM

3D CT Liver Images Lesion Extraction and Classification for ...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： S. Jayarathna R. Sowmiya G. Nallasivan G. Sahaana S. Ashok R. Saravanakumar Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The imaging technique known as computed tomography (CT) is often considered to be the most reliable way for non-invasive diagnosis. Through the use of three-dimensional (3D) computed tomography images, we were able to categorize aggressive tumors that were found in the liver for the aim of this research. When it comes to the three-dimensional (3D) CT image, the dimension reduction is accomplished by the use of principal component analysis (PCA). The Random Forest Algorithm is responsible for identifying the abnormal hepatic region, and a median filter is used to reduce the amount of noise occurring. Methods of data augmentation are also employed in this process. To extract Gray Level Run Length Matrix (GLRLM) features, using Pyradiomics python package. These features are then fed into a 3D-CNN classifier to classify malignant tumors and haemangiomas. Our model was found to have greater performance after being compared to the findings of other models. This was revealed after the comparison was made. For the training dataset, our model has an accuracy of 97.2%, and for the test dataset, it has an accuracy of 98.25%. This accuracy is determined by the performance metrics that our model incorporates.

关键词： Training Solid modeling Accuracy Three-dimensional displays Computed tomography Liver Feature extraction Data augmentation Data models Principal component analysis

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Highly responsive nonlinear sensor by tracking a Hamiltonian hopping point

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Photonics Research 2024年第10期12卷 2235-2241页

作者： ZHU LIU YIKUAN DENG XI TIAN ZHIPENG LI Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory of Physics and Devices in Post-Moore EraCollege of Hunan ProvinceSchool of Physics and ElectronicsHunan Normal UniversityChangsha 410081China Tsinghua-Berkeley Shenzhen Institute Institute of Data and InformationShenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

Responsivity is a critical parameter for sensors utilized in industrial miniaturized sensors and biomedical implants,which is typically constrained by the size and the coupling with external reader,hindering their widespread applications in our daily ***,we propose a highly-responsive sensing method based on Hamiltonian hopping,achieving the responsivity enhancement by 40 folds in microscale sensor detection compared to the standard *** implement this sensing method in a nonlinear system with a pair of coupled resonators,one of which has a nonlinear ***,our method surpasses the sensing performance at an exceptional point(EP)—simultaneous coalescence of both eigenvalues and *** responsivity of our method is notably enhanced thanks to the large frequency response at a Hamiltonian hopping point(HHP)in the strong coupling,far from the *** study also reveals a linear HHP shift under different perturbations and demonstrates the detection capabilities down to sub-picofarad(<1 pF)of the microscale pressure sensors,highlighting their potential applications in biomedical implants.

关键词： Hamiltonian nonlinear point

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security controls

arXiv

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arXiv 2024年

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Dept. of Computer Information Systems Texas A&M University - Central Texas TX United States College of Business University of Tampa Florida United States Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamper-proof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enab

关键词： Blockchain

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