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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Anbalagan, E. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai600062 India St. Joseph's Institute of Technology Information Technology OMR Chennai600119 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Supervised deep learning has brought changes to data analysis and prediction function by leveraging its capacity to make many number of relationships with heterogeneous forms of data. This paper compares deep learning techniques like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks in three tasks: There are three types of tasks, including image classification, time-series prediction, and text classification. In the picture simulation of the CIFAR-10 set for training, the accuracy of the ResNet model was 95% while the accuracy of VGG16 w 93%. When applied to the M4 competition dataset in time-series prediction, the LSTM model was superior to other benchmarks with MAE of 0.40 and RMSE of 0.60, with the RNN model giving MAE of 0.50 and RMSE of 0.65. It is 0.92 of accuracy, 0.91 of F1 score, and 0.921 for precision using the IMDB reviews dataset in text classification of movies using the CNN-LSTM hybrid model while the Bi-LSTM only had an accuracy of 0.89 and an F1 score of 0.889. Even though models, such as ResNet and Bi-LSTM, took slightly more time to train-Approximately 5.0 hours-it was because the epoch models were significantly superior to the baseline model. The present work focuses on the benefits of deep learning architectures in providing interpretable patterns in numerous applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on the Method of Color Compensation and Underwater Image Restoration Based on Polarization Characteristics 3

Research on the Method of Color Compensation and Underwater ...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and Applications, CVIDL and ICCEA 2022

作者： Gong, Kaiqiang Hua, Dou Hefei University of Technology School of Computer Science and Information Engineering AnHui Heifei China

ISBN: (纸本)9781665459112

The attenuation degree of light in different bands is different when transmitting underwater, which leads to the problems of color distortion and blurred target details in the turbid underwater image. Combined with Monte Carlo simulation and measured experiments, this paper studied the polarization characteristics of light transmission in different wavelength bands underwater. The results show that the longer the wavelength of light, the stronger the ability of polarization retention underwater. Based on this feature, a new method of color compensation of underwater images is proposed. By fusing the polarization image with the intensity image, the exposure of the low-brightness areas in the image is improved, so as to compensate for the areas with serious color distortion in the underwater image. And then the image is deblurred and enhanced by using the dark channel prior principle. Comparative experimental results indicate that the method can effectively compensate for the color distortion of underwater images and significantly improves the visibility of underwater targets. © 2022 IEEE.

关键词： Image reconstruction

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Decentralized MISO Cooperative Beamforming Design for Multi-Cell URLLC Network 7

Decentralized MISO Cooperative Beamforming Design for Multi-...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Wang, Lihui Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

ISBN: (纸本)9781665478571

Achieving low-latency for ultra-reliable and low- latency communications (URLLC) is one of the major challenge for the fifth-generation cellular networks. To address this challenge, the cooperation among base stations (BSs) has the potential to reduce latency and improve data speed. However, it requires a great quantity information interaction at cost of magnanimous utilization of the backhaul. In this paper, the author analyzes the duality between virtual uplink and downlink optimization problems. then, we propose an iterative decentralized algorithm to solve a max-min achievable rate problem based on the joint of the alternating direction method of multipliers(ADMMs) and successive convex approximation (SCA) method. Specifically, we can iteratively approximate the nonconvex problem into a convex problem by using the SCA method. In the second step, the ADMM method is used to transform the centralized optimization problem into a decentralized optimization problem that solves the problem locally. Each BS is regarded as a subproblem to work the problem out locally in parallel. By allowing BSs to exchange the required information until ADMM converges, the resulting formulation is optimized. Finally, simulation results show the effectiveness of our proposed algorithm. © 2022 IEEE.

关键词： Optimization

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Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

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computer systems science & engineering 2023年第6期45卷 2447-2460页

作者： Ahmed Zohair Ibrahim P.Prakash V.Sakthivel P.Prabu Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhP.O.Box 8442811671KSA School of Computer Scence and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India

In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is *** has been considered in earlier times with the support of traditional *** learning process has also been widely considered in these genomics data processing *** this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional ***,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)*** suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage *** DBNJZZ gives better performance than previously available methods.

关键词： Deep belief networks zig zag deep learning mean absolute percentage error mean absolute error root mean square error DNA genomics

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Physically Secure and Privacy-Preserving Authentication Scheme for VANET

Physically Secure and Privacy-Preserving Authentication Sche...

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2024 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2024

作者： Chen, Dongmei Liang, Yangfan Li, Lihui Liu, Yining School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China

ISBN: (纸本)9798331507886

The communication between vehicles and other entities occurs over open wireless networks, making them susceptible to various security attacks In Vehicular Ad Hoc Network (VANET). Consequently, many researchers have proposed numerous data transmission and key exchange protocols to ensure secure communication within VANET. The keys and sensitive information involved in these protocols need to be stored long-Term in the tamper-resistant devices of vehicles. However, due to the limited resources of these devices, many encryption schemes are difficult to embed. Even if effective encryption schemes can be embedded in VANET, the long-Term storage of data in vehicles still carries a significant risk of being attacked. To tackle this problem, we propose an authentication protocol designed for VANET that emphasizes both physical security and privacy protection. The protocol primarily leverages the properties of Physical Unclonable Functions to achieve enhanced security. This approach eliminates the need for vehicles to store keys and sensitive messages long-Term, reducing the storage burden on vehicles while enhancing the system's security and efficiency. Detailed security analyses demonstrate that our proposed scheme achieves the goals of physical security and privacy protection. A comparison with similar approaches demonstrates that our solution outperforms them in both time efficiency and communication overhead. © 2024 IEEE.

关键词： Authentication Protocol

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Research on natural environment protection based on blockchain technology to facilitate ecological balance and regional revitalization 4

Research on natural environment protection based on blockcha...

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4th International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Wan, Chenye Fu, Taoran Wang, Haochen Wang, Xing Luo, Junyou Wang, Ziqian School of Information Science and Technology Nantong University Nantong China School of Computer and Communication Hunan Institute of Engineering Xiangtan China School of Computational Science and Electronics Hunan Institute of Engineering Xiangtan China School of Robotics Hunan University Changsha China

ISBN: (纸本)9798350341690

Now the Masai Mara population continues to expand, the wildlife in the reserve is facing serious threats to their survival. The purpose of this report is to establish a management strategy model for local protected areas that balances the mutual interests between local people and wildlife in ***, Masai Mara divides the protected area into three parts, planting area, protected area, and livestock area, and then conducts correlation analysis on different areas, and concludes that the reduction in the number of wild animals is closely related to the degree of population density, and the area of cultivated land and forest area is highly correlated. So a nonlinear programming model of multiple strategies is established to plan the planting area. Then establish a comprehensive evaluation model based on the entropy weight method, quantify various strategies, and use gray correlation prediction to obtain solutions. This article introduces blockchain related technologies and uses its decentralization, security and transparency to build an architecture that contributes to the actual government protection of wild animals. © 2023 IEEE.

关键词： Conservation

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An Adaptive Attitude Controller for A Quadrotor Transporting the Payload With Unknown Mass 38

An Adaptive Attitude Controller for A Quadrotor Transporting...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Qiu, Shihao Chen, Longlong Li, Shuang Lv, Zongyang Tang, Huayi Dalian University of Technology School of Computer Science and Technology Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China Henan University of Technology College of Information Science and Engineering Zhengzhou China Dalian University of Technology School of Mechanical Engineering Dalian China

ISBN: (纸本)9798350303636

An adaptive control approach is presented in this article to deal with the impacts of carrying a payload with unknown mass by a quadrotor. This approach can effectively control the quadrotor's attitude stability. The paper commences by presenting the dynamic model of the quadrotor, followed by the development of a controller to manage its attitude control. Through Lyapunov analysis, the stability of the controller is validated. Ultimately, the proposed controller and a backstepping controller are used to controlling the attitude of the quadrotor, respectively, and experimental outcomes substantiate the efficiency and feasibility of the introduced controller. © 2023 IEEE.

关键词： Adaptive Control Quadrotor Unmanned Aerial Vehicle Unknown Mass Payload

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Intelligent Tutoring for Large-Scale Personalized Programming Learning Based on Knowledge Graph 53

Intelligent Tutoring for Large-Scale Personalized Programmin...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Li, Ying Qiu, Jincheng Yang, Runze Zhu, Tongyu Sheng, Hao Gui, ShiJie Liang, Yu School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (纸本)9798350336429

The development of learning analytics technology has created the necessary conditions for the implementation of large-scale personalized education. By taking advantage of the current advancements available, we can adopt a deep integration of 'artificial intelligence + education', take educational big data as the research object, aim at achieving precise teaching, and use cognitive diagnosis as a means to construct an intelligent guidance model. The model will conform to a learner's cognitive patterns and curriculum characteristics to meet his personalized learning needs under different knowledge states. Firstly, use knowledge map and abstract syntax trees to extract the characteristics of teaching objectives from questions and standard programing codes. Secondly, Use 'target characteristics - learning characteristics - learning sequence - learning effect' cognitive diagnosis model to assess student achievement in learning. Finally, according to the correlation between knowledge points, the initial learning path consisting of all relevant knowledge points is identified from a knowledge map. And the most optimal learning path is selected based on the importance of knowledge points and student's learning situation. There are three innovation points in this paper. First, the traditional method which only focuses on discovering the one-dimensional knowledge features implicated in the questions, is extended to construct a knowledge-ability multidimensional feature matrix. Secondly, it optimized static cognitive diagnosis into dynamic cognitive arbitrariness, which has a modeling capability of dynamic temporal sequence. Thirdly, it regarded the correlation between knowledge points, and the importance of knowledge points as the heuristic conditions for constructing a learning path. © 2023 IEEE.

关键词： Cognitive Diagnosis Intelligent Guidance Model Knowledge Graph Personalized Learning Path

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Multi-Population Genetic Algorithm Based EFSM Regression Test Data Generation 23

Multi-Population Genetic Algorithm Based EFSM Regression Tes...

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23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Li, Jiahao Zhang, Bo Fan, Zeyu Wang, Yichen School of Reliability and Systems Engineering Beihang University Beijing China Tianjin University Computer Science and Technology Division of Intelligence and Computing China

ISBN: (纸本)9798350359398

Extended Finite State Machine(EFSM) has a wide range of application systems and protocols in control modeling. Genetic algorithms are commonly used in EFSM test data generation due to their good performance. Studies have been done to generate regression test sequences to meet the requirements based on changes in EFSM, there is a large amount of historical test data to choose from when using genetic algorithms to generate test data for this part of the test sequence, and there is no research on how to fully utilize the existing test data to generate regression test data for the EFSM. In this paper, a multi-population genetic algorithm is designed to generate test data to meet the requirements. Firstly, some test data that can cover the regression test sequences are selected from the historical test data, and for the rest of the test sequences, a mathematical model for the simultaneous optimization of multiple test sequences is constructed, secondly, in order to make full use of the historical test data, the relationship between the existing test data and the target test sequences is investigated, and the initial population is constructed based on the similarity of the test sequences and the FSCS-ART algorithm, which makes full use of the The initial population is constructed based on the similarity of test sequences and the FSCS-ART algorithm, which makes full use of the existing test data and ensures the diversity of the initial population. Finally, the adaptive crossover operator and variation operator were designed and the fitness function was optimized. The experimental results show the feasibility and efficiency of this method in test data generation. © 2023 IEEE.

关键词： Software testing

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