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Intelligent Task Offloading and Collaborative Computation over D2D Communication

China Communications 2021年第3期18卷 251-263页

作者： Cuili Jiang Tengfei Cao Jianfeng Guan Department of Computer Technology and Applications Qinghai UniversityXining 810016China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and *** the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation ***,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high ***,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,***,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

关键词： utility maximization lyapunov optimization task offloading mobile edge computing

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Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach

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Intelligent Automation & Soft Computing 2023年第4期36卷 1189-1203页

作者： T.Edwin Ponraj J.Charles Department of Computer Applications Noorul Islam Centre for Higher EducationKumaracoil629180India Department of Software Engineering Noorul Islam Centre for Higher EducationKumaracoil629180India

The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical *** wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential muta-tions,computational approaches that can reliably anticipate the consequences of amino acid mutations are *** work presents a robust methodology to analyze and identify the effects of mutation on a single protein ***,the context in a collection of words is determined using a knowledge graph for feature selection *** proposed prediction is based on an easier and sim-pler logistic regression inferred binary classiﬁcation *** approach can able to obtain a classiﬁcation accuracy(AUC)Area Under the Curve of 87%when randomly validated against experimental energy ***,for each cross-fold validation,the precision,recall,and F-Score are *** results support the validity of our strategy since it performs the vast majority of prior studies in this domain.

关键词： Proteins data science mutation analysis random forest neighbor proteins single and double mutations

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Explainable Artificial Intelligence Approaches: Challenges and Perspectives

Explainable Artificial Intelligence Approaches: Challenges a...

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2021 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", T and QM and IS 2021

作者： Shevskaya, Natalya V. Saint Petersburg Electrotechnical University 'LETI' Software Engineering and Computer Applications Department Saint Petersburg Russia

ISBN: (纸本)9781665433020

Artificial intelligence systems are used in many areas, for example, in finance and medicine. Every year, intelligent systems get for processing more and more data and make more and more decisions. All these decisions are exerting more and more influence on human destinies. Mistrust of fully non-human, autonomous artificial intelligence systems become the cornerstone. The key of mistrust is hidden in the lack of understanding as to why intelligent systems make this or that decision. To solve this problem, methods of explainable artificial intelligence are used. This article provides an overview of such methods and discusses the main trends in their development, as well as the implementation difficulties that one has to face during development and use. © 2021 IEEE.

关键词： Intelligent systems

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Two-branch Convolutional Networks for Stress Detection from Biomedical Data 27

Two-branch Convolutional Networks for Stress Detection from ...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Dobrokhvalov, Maksim O. Filatov, Anton Yu. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350363708

Stress is considered one of the main causes of health problems. Combining physiological signals from multiple modalities is a promising method for more accurately determining a person's condition. This paper proposes single-branch and two-branch convolutional neural networks for stress detection using biomedical data (electrocardiogram, blood pulse pressure, electrodermal activity). The two-branch model independently processes heart data (electrocardiogram or blood volume pulse) and electrodermal activity, and then merges the results of the branches using a predictor. The work examines various predictors. In some predictors, only the final result of the network is transferred to the loss function, and in one, the results of each branch and the whole network are taken into account. On the WESAD dataset, the single-branch model achieves an accuracy 0.983 and an F1 score 0.976, and the two-branch model achieves an accuracy 0.994 and an F1 score 0.992. On the UBFC-Phys dataset, the single-branch model achieves an accuracy of 0.922 and an F1 score of 0.945. © 2024 IEEE.

关键词： Electrocardiograms

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Investigation of Local and Global Methods of Explainable Artificial Intelligence. Metrics for Evaluating Explanation Methods 3

Investigation of Local and Global Methods of Explainable Art...

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3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022

作者： Shevskaya, Natalya V. Shishkin, Ivan V. Stepanov, Vladislav D. Oblizanov, Alexander D. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9781665467766

Artificial intelligence systems in meaning of machine learning face not only technical (collecting enough data, efficient hardware implementation), but also social difficulties in implementation. The reason for this is distrust due to a lack of understanding by end users how such systems work and why they make certain decisions. Therefore, this paper discusses the methods of explainable artificial intelligence aimed at solving the problem of misunderstanding, also considers existing approaches to the evaluation of these methods and determines the requirements for the evaluation of AI system and the metrics that can be used in it. It will also answer the question that runs through this work - what is the role of hybrid intelligence in the tasks of explaining models? © 2022 IEEE.

关键词： Lime

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Improving Transfer Learning Performance for Abnormality Detection in Brain MRI Images Using Feature Optimization Techniques 27

Improving Transfer Learning Performance for Abnormality Dete...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Nizamli, Yasser A. Filatov, Anton Yu. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350363708

Accurate and early diagnosis of brain tumors plays a vital role in determining appropriate treatment and increasing survival rates. Manual diagnosis using MRI images is often prone to errors and can be a tedious and time-consuming procedure. As a result, automated computer-aided systems that utilize artificial intelligence and machine learning techniques are widely used. However, accurately diagnosing the type of brain tumor remains a significant and complex challenge for today's medical imaging research community. In this paper, we present an enhanced transfer learning approach that aims to differentiate brain tumor types using MRI scans while achieving state-of-the-art performance. In the proposed approach, the pre-trained VGG-19 network with fixed weights is used to map MRI scans to a high-level numerical representation. The extracted features are then passed to a support vector machine algorithm to classify brain tumors into one of three major categories: glioma, meningioma, and pituitary. To further improve the performance of the model, the effectiveness of utilizing feature optimization techniques based on the radial basis similarity function and L1-penalty logistic regression was investigated. For evaluation, the benchmark Figshare dataset containing 3064 MRI images was used after a series of processing operations. The proposed system achieved a high overall accuracy of 98.53%, outperforming other analogues. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Application of Text Analysis Methods to Recommend Student Choices 25

Application of Text Analysis Methods to Recommend Student Ch...

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25th International Conference on Soft Computing and Measurements, SCM 2022

作者： Korytov, Pavel V. Kholod, Ivan I. Saint-Petersburg Electrotechnical University LETI Department of Software Engineering and Computer Applications Saint-Petersburg Russia

ISBN: (纸本)9781665496698

The paper considers the use of natural language processing methods to recommend student choices. Descriptions of subjects taken by the student serve as the basis for compiling the student profile. This profile is used to rate available side projects as most corresponding to that student's educational history. The quality of recommendations is evaluated based on the data from the Individual Educational Trajectories system. © 2022 IEEE.

关键词： Students

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Research of Dimension Reduction Algorithms of Feature Space in Data Analysis Task

Research of Dimension Reduction Algorithms of Feature Space ...

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2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic engineering, EIConRus 2020

作者： Popov, Nikita V. Klionskiy, Dmitry M. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications St. Petersburg Russia

ISBN: (纸本)9781728157610

Today data describing a particular subject area, often contain a large number of different features that determine the properties of any processes or objects. At the same time, these data sets can reach enormous sizes, so working with data turns into very resource-intensive and long processes. Reducing the dimension of the feature space entails a reduction in the used memory and getting rid of noisy and duplicate information. To reduce the space, various machine-learning algorithms are used, each of which has its own individual degree of efficiency and validity for its area of application. Client databases are no exception, they often contain large amounts of information, some of which are redundant. Therefore, reducing the space of signs in client data is a topic whose relevance is only increasing every day. This paper presents a study of such methods as reduction of features with low variability, Univariate feature selection, recursive feature elimination, selection based on a decision tree, principal component analysis. Based on the results of the study, requirements are formulated for an algorithm for reducing the dimension of the feature space specifically for the task of working with client data. © 2020 IEEE.

关键词： Python

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On the impact ionization in double-gate MOSFET using full band monte carlo method

On the impact ionization in double-gate MOSFET using full ba...

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2008 8th IEEE Conference on Nanotechnology, IEEE-NANO

作者： Bai, Ping Chang, Ken Kajen, R.S. Li, Erping Samudra, Ganesh Engineering Software and Applications Institute of High Performance Computing Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9781424421046

We predict the impact ionization in a 13nm gate length double-gate MOSFET structures using full-band self-consistent ensemble Monte Carlo method with quantum correction. The effects of various design parameters such as gate oxide thickness, body thickness and channel length on the impact ionization onset voltages are investigated. The studies show that the reduction of channel length reduction remains as the major contributor to the impact ionization phenomena. On analyzing the distribution of impact ionization events in the structure, impact ionization induced hot carrier activity is shown to be largely confined to the channel-drain interface region. This allows for possible gate oxide hot carriers reliability issues to be greatly reduced through an optimal design. © 2008 IEEE.

关键词： Monte Carlo methods

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Explainable artificial intelligence methods based on feature space analysis 4

Explainable artificial intelligence methods based on feature...

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4th International Conference on Control in Technical Systems, CTS 2021

作者： Popov, Nikita V. Shevskaya, Natalya V. Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University "LETI" St. Petersburg Russia

ISBN: (纸本)9781665424547

In the 21st century, mankind is actively introducing machine learning and artificial intelligence into all spheres of life. But most modern algorithms output the final result of the calculations without revealing the details of obtaining the result, which is the reason for some skepticism towards it. To correct this situation, there is a need to use understandable machine learning methods that increase the transparency of use and the level of trust of people. The work reviews existing solutions to this problem, and also draws a conclusion on the effectiveness of a particular algorithm. Based on the results of the article, ways to further develop the work are proposed. © 2021 IEEE.

关键词： Lime

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