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Random Fuzzy Production and Distribution Plan of Agricultural Products and Its PSO Algorithm

Random Fuzzy Production and Distribution Plan of Agricultura...

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2014 ieee international conference on progress in informatics and computing

作者： Ruiying Liu Xuejie Bai College of Science Agricultural University of Hebei

We present a random fuzzy tactical planning model for the production and distribution of agricultural products because of the uncertainties encountered in the whole process. In the proposed model, the crops' yields are uncertain and assumed tobe characterized by random fuzzy variables with known probability and possibility distributions. The model approach selected is a two-stage hybrid uncertain programming in which the decisions in a first stage are designed tomeet the uncertain outcomes in a second stage. Since itis always difficult tohandle with the random fuzzy model directly, we apply an approximation approach(AA)toevaluate the value of the objective function and discuss the convergence the AA. Considering that the approximating model is neither linear nor convex, it cann't be solved via the conventional optimization algorithm. Therefore, an approximate-based hybrid particle swarm optimization(PSO) algorithm is designed tosolve the proposed model. Finally, an application example is presented tohighlight the significance of the random fuzzy production and distribution model as well as the effectiveness of the solution method.

关键词： agricultural products production and distribution plan random fuzzy programming approximation approach particle swarm optimization

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A Minimum Delay Link Scheduling Algorithm in Vehicular Ad-Hoc Networks

A Minimum Delay Link Scheduling Algorithm in Vehicular Ad-Ho...

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2014 ieee international conference on progress in informatics and computing

作者： Yong Zhang Junyi Wang Jiming Lin Key Laboratory of Cognitive Radioand Information Processing Ministry of EducationGuilin University of Electronic Technology Guangxi Experiment Center of Information Science Guilin University of Electronic Technology

Reducing the information transmission delay in the Vehicular Ad Hoc Networks(VANETs) can improve the timeliness of receiving information and avoid the happening of the accident. As a potential solution, cooperative transmission,which may effectively reduce the transmission delay by exploiting spatial diversity, has attracted lots of attentions recently. In this paper, we modeled the information transmission delay minimization of the VANETs in which cooperative transmission is allowed, and proposed a gradient projection algorithm based on the model. The RSU can schedule the vehicle transmission behavior according tothe algorithm, soas tosolve the problem of the information transmission delay minimization in the VANETs where multipath routings(MRTDM) are ***, our simulation results show that the algorithm not only has good convergence, but alsocan solve the MRTDM in VANETs.

关键词： vehicular ad-hoc networks cooperative transmission gradient projection algorithm delay minimization

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An Improved Watermarking Algorithm Using Adaptive Threshold

An Improved Watermarking Algorithm Using Adaptive Threshold

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2014 ieee international conference on progress in informatics and computing

作者： Guiduo Duan Yueqiang Liu Xi Zhao School of Computer Science and Engineering University of Electronic Science and Technology of China College of Computer Science and Information Engineering Tianjin University of Science and Technology

An improved semi-fragile algorithm incorporated the generalized Benford’s law in the wavelet-based contourlet transform（WBCT） is proposed in the paper. In the past research the majority of algorithms frequently used a pre-determined threshold totolerate the errors caused by some non-malicious manipulations. In contrast tothe conventional methods, we use the adaptive thresholds instead of a fixed one for improving the accuracy of the tampering region detection. The adaptive process is based on the relationship between the compression ratio（CR）of JPEG2000 and threshold. Using the generalized Benford’s law,once the CR is determined, the threshold can be adapted. The tampering detection rate is used toevaluate the performance of our scheme. Simulation results showed that our improved method can localize the malicious tampered region with more high accuracy.

关键词： semi-fragile watermarking wavelet-based contourlet transform the generalized Benford’s law

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Adaptive Fine-Tuning and Feature Enhancement of SAM for Remote Sensing Instance Segmentation

Adaptive Fine-Tuning and Feature Enhancement of SAM for Remo...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Zhixin Zhao Yangguang Liu Lingwu Meng Haochen Zhang Mingyang Zhu Liang Xiao School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Remote sensing image instance segmentation aims to accurately separate independent objects and automatically identify land attributes. Recent advancements in large models have propelled self-supervised learning, especially with the segment anything model (SAM). Although SAM can generate instance segmentation masks without additional training, its application to remote sensing images still faces challenges, such as a severe reliance on manual prior prompts, inaccuracies in detail segmentation, and insufficient generalization capability. Therefore, to enhance the generalization capability of SAM in the field of remote sensing images, we propose a SAM-based fine-tuning model called AFFE-SAM. This model combines adaptive low-rank (AdaLoRA) and variable adapter (MiMi-Adapter) fine-tuning methods. Additionally, it improves mask quality in the decoder by employing adaptive multi-scale feature enhancement. We conducted experiments on two datasets, obtaining mAP scores of 66.5 for the NWPU VHR-10 dataset and 67.1 for the SSDD dataset. The evaluation results validate the effectiveness of our method.

关键词： Instance segmentation Training Adaptation models Self-supervised learning Propulsion Decoding Sensors Object recognition informatics Remote sensing

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Pathological Brain Detection Based on Online Sequential Extreme Learning Machine

Pathological Brain Detection Based on Online Sequential Extr...

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2016 ieee international conference on progress in informatics and computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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Personalized Learning Design of Ideology and Politics of Distance Education Courses Based on Big Data

Personalized Learning Design of Ideology and Politics of Dis...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Junyan Zhao Xubiao Yang Qian Qiao Liqiong Chen School of Network Education East China University of Science and Technology Shanghai China College of Air Transport Shanghai University of Engineering Science Shanghai China School of Social and Public Administration East China University of Science and Technology Shanghai China Shanghai Institute of Technology Shanghai China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

As an important position in education, distance education is a new subject faced by the education community under the new situation. In view of the problem of "islandization" in the process of ideological and political education in distance education based on big data technology, this paper proposes a personalized learning service framework for distance education students' ideological and political courses, including data management, data analysis layer and service provision. At first, a large amount of remote education course ideological and political data is collected through the network, database and various mobile platforms. After the data is preprocessed, knowledge graphs and data mining techniques are used to mine the ideological and political elements of the course and develop personalized ideological and political learning program. Distance education curriculum ideology and politics are supported to teach students according to their aptitudes, and focus on students' individualized ideology and politics learning, then improve the teaching effect of distance education ideology and politics lessons.

关键词： Data analysis Databases conferences Big Data Educational courses Data mining informatics

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TransRESCAL: A Dense Feature Model for Knowledge Graph Completion

TransRESCAL: A Dense Feature Model for Knowledge Graph Compl...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Panfeng Chen Yisong Wang Quan Yu Qing Fan Guizhou University Guiyang China Qiannan Normal University for Nationalities Qiannan China Jingchu University of Technology Jingmen China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Knowledge Graph Completion (KGC) is a hot-spot in artificial intelligence research. Dozens of hundreds of models are devised to this end. The translation-based and factorization- based approaches are very popular ones for their efficiency and simplicity. TransE and RESCAL are the canonical ones in both research lines. However, the simplicity also limits the expressivity of the model to achieve better performance. To improve the results, we couple the two approaches to form a more expressive one named TransRESCAL. It is capable of distinguishing head from tail entity embedding due to TransE has the ability to be capable of doing this. Therefore, the proposed model is capable of clarifying the fact that the position of head and tail entity embeddings in score function of factorization-based models. It is beneficial for further understanding the model for KGC task. Hence, the proposed model is not only capable of capturing structural feature by TransE but also semantic ones by RESCAL. It potentially captures denser features than every single model, which is a competitive alternative to the deep neural network (DNN)-based models for KGC tasks.

关键词： Measurement Knowledge engineering Semantics Neural networks Mathematical model Task analysis informatics

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Refined Learning of Hidden Markov Models with a Modified Baum-Welch Algorithm and Informative Components

Refined Learning of Hidden Markov Models with a Modified Bau...

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The 2010 ieee international conference on progress in informatics and computing

作者： Michael G.Madden School of Engineering and Informatics National University of Ireland Galway GalwayIreland

Methods for refined learning of hidden Markov models(HMMs) have been studied for decades,taking either structural or parametric *** paper discusses using informative components of state parameters to refine the training of *** the purpose of HMM learning is to decompose a high-dimensional observation space into latent states,we may still end up with high dimensional state models.A problem with such high dimensional state models is that if they are learned with limited amounts of training data,this will bias the likelihood during training,particularly when there are outliers in the observation *** propose a modified Baum-Welch algorithm to estimate the HMM parameters over informative components selected with nearly sufficient statistics, using relative entropy *** method produces a modified likelihood estimation in reduced dimensions,which decreases the computation complexity and preserves near-optimality of the HMM that is *** variations on the algorithm are discussed,one that deals with general state model and another that considers a special Gaussian *** approach is evaluated with a problem of using pen tip trajectory for handwriting character verification and *** data sets are studied,one with low dimensionality and the other with high dimensionality.A comparison is performed of the results of using HMMs trained without and with informative state *** both data sets,we found that our approach yields improvements in *** error rate varies with relative entropy change is also evaluated.

关键词： hidden Markov model Baum-Welch algorithm relative entropy handwriting recognition

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Analysis and Prediction of Computer Science Students' Performance Based on Core Foundational Courses using Ensemble Machine Learning Methods

Analysis and Prediction of Computer Science Students' Perfor...

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international ieee conference on Intelligent Systems, IS

作者： Betty Mayeku Juma Kilwake Ambrose Osala Mamai Eve Khatundi Wamasebu School of Computing and Informatics Kibabii University Bungoma Kenya

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

As the surge toward a technology-driven and dependent society increases, the demand for technology oriented workforce particularly computer science continues to rise. However, the concern that continues to persist in a number of higher institutions of learning is the low graduation rates and poor performance in computer science programs. This study, carried out in a Kenyan public university attempted to extend the understanding on the performance of computer science students The study aimed at examining whether performance in early foundational core courses had any influence on the final graduation score (through final accumulative score) among computer science students using ensemble machine learning methods. The study specifically employed Random Forest and XGBoost ensemble methods. The results showed the Random forest regression model having a prediction power of 83.20% while XGBoost had a slightly improved prediction power of 84.6%. The results also revealed better model performance when using only years 1 and 2 core courses as compared to using years 1 to 3 core courses. This is an indicator that foundational core courses are good predictors of student academic performance in computer science programs. This work in progress is part of a preliminary study aimed at addressing the challenge of low graduation rates and poor performance in computer science programs.

关键词： Computer science Power measurement Computational modeling Predictive models Ensemble learning Surges Intelligent systems Random forests

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CBR Based Emergency Case Generating System under Engineering Accidents

CBR Based Emergency Case Generating System under Engineering...

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The 2010 ieee international conference on progress in informatics and computing

作者： Chunguang Chang~(1,2) 1:School of Management Shenyang Jianzhu University Shenyang,China Xiang Ma~1,Xiaoyu Song~1,Bo Gao~1 2:Key Laboratory of Industrial informatics Shenyang Institute of Automation,Chinese Academy of Sciences Shenyang,China

With the flourishing development of construction and real estate industry,engineering accidents happen more *** purpose of this paper is to generate emergency case as quick as possible so as to improve the quick emergency reaction capability when engineering accident *** flow of CBR based emergency case generating system(CBR/ECGS) under engineering accidents is *** the static and dynamic case presentation technologies for CBR/ECGS are designed,and the case retrieval,case adaptation and case maintenance technologies for CBR/ECGS are *** can be seen from running result of CBR/ECGS that,comparing with the result under the mode in which artificial adjustment is allowed, the overcast degree of emergency cases generated by CBR/ECGS under the mode in which only automatic system adjustment is allowed is almost the *** validity of CBR/ECGS proposed in this paper is validated,and CBR is suitable for solving complex,non-structure problems such as emergency case generating under engineering accidents.

关键词： emergency case engineering project accidents case-based reasoning

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