检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Du, Zhi-Gang Pan, Jeng-Shyang Wang, Xiao-Yang Chu, Shu-Chuan Ni, Shao-Quan School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031 China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031 China

Meta-heuristic evolutionary algorithms have been widely used in global optimization problems. Still, the global optimization of meta-heuristic evolutionary algorithms relies on a large number of frequent fitness evaluations, which hinders their application in expensive and time-consuming real-world issues. For example, in a large-scale transportation network, due to the large search space and high computational complexity, the performance of the algorithm is degraded. After years of research, many scholars have proposed the idea of using the Surrogate-assisted model to reduce the number of real evaluations for expensive problems and solve the time-consuming global optimization problem in reality. To solve the high-dimensional and time-consuming problem, in reality, a two-layer Surrogate-assisted Fish Migration Optimization (SA-FMO) is proposed. The global surrogate-assisted model is an accurate fit for the entire search space. The local surrogate-assisted model is to capture the topographic details near the current optimal solution position and improves the accuracy estimation of local optimization. In order to verify the effectiveness of our proposed algorithm, this paper first tests six benchmark functions in 50 dimensions. Finally, The optimization of long and short routes in urban rail transit, also known as the Train Routing Optimization problem, is modeled and optimized using the SA-FMO algorithm to improve operational efficiency and accelerate vehicle turnover in the case of unbalanced passenger flow distribution in urban rail transit interruption surfaces. © 2022, The Authors. All rights reserved.

关键词： Global optimization

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Effects of alloying element on the mechanical behavior of Mg-MMCs: A review

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Materials Today: Proceedings 2023年

作者： Walke, Santosh Kale, V.M. Patil, Pravin P. Mohan Giri, Jitendra Kumar, Harish Kumar, Manish Arun, Vanya Department of MIE College of Engineering National University of Science and Technology Muscat Oman Department of Mechanical Engineering Indira College of Engineering and Management Affiliated to Savitribai Phule Pune University Pune India Department of Mechanical Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Mechanical Engineering Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida201308 India Division of Research & Innovation Uttaranchal University Dehradun Uttarakhand248007 India School of Computer Application Lovely Professional University Phagwara India Department of Electronics & Communication Engineering IILM University Greater Noida201308 India

Due to their superior mechanical qualities and high strength-to-weight ratio when compared to unalloyed magnesium, magnesium alloys are widely used in a variety of applications. While maintaining control over the rate of corrosion for materials made of magnesium is crucial in the biological environment. Prior to controlling the degradation rate, Alloying is evaluated as the most essential way to enhance the corrosion and mechanical characteristics of the composites on comparing to the unalloyed material. Therefore, it is a pre-requisite to decide on the suitable alloying element to be added to the metal matrix composite that has a positive influence on the material property. In selecting the alloying elements, biocompatibility, biodegradability, less stress shielding effect, bio-activeness, and non-toxicity are the important parameters for biomedical applications other than mechanical and corrosion properties. The current study evaluated the various alloying elements in the Mg-based materials including Al, Ca, Al, Zn, rare earth (RE), Mn, Bi, Zr, Si, Ag, Zn, Ca, etc in a detailed version. © 2023

关键词： Alloying elements

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Automatic Construction of Parallel Algorithm Portfolios for Multi-objective Optimization

arXiv

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arXiv 2022年

作者： Ma, Xiasheng Liu, Shengcai Hong, Wenjing School of Mechanical Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction Beijing100083 China Singapore Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

It has been widely observed that there exists no universal best Multi-objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-objective Optimization Problems (MOPs). In this work, we advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple MOEAs independently in parallel and gets the best out of them, to combine the advantages of different MOEAs. Since the manual construction of PAPs is non-trivial and tedious, we propose to automatically construct high-performance PAPs for solving MOPs. Specifically, we first propose a variant of PAPs, namely MOEAs/PAP, which can better determine the output solution set for MOPs than conventional PAPs. Then, we present an automatic construction approach for MOEAs/PAP with a novel performance metric for evaluating the performance of MOEAs across multiple MOPs. Finally, we use the proposed approach to construct a MOEAs/PAP based on a training set of MOPs and an algorithm configuration space defined by several variants of NSGA-II. Experimental results show that the automatically constructed MOEAs/PAP can even rival the state-of-the-art ensemble MOEAs designed by human experts, demonstrating the huge potential of automatic construction of PAPs in multi-objective optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Parallel algorithms

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Breast Cancer Classification in Mammograms Using Support Vector Machines (SVMs) and Histogram of Oriented Gradients (HOG)

Breast Cancer Classification in Mammograms Using Support Vec...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Rashmi Choudhry Sivadharshini B Madhav Dua V. Nirmala Saif O. Husain R. S. Arunkumar Department of Information Technology IES University Bhopal M.P. India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Coimbatore Tamilnadu Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This work focuses on breast cancer classification in mammograms using SVMs classifier and histogram of oriented gradients features. The envisioned system is designed in such a way to enhance the diagnostic precision through integration of the virtues of both SVMs and HOG in identifying discriminative characteristics of breast tissue. Special emphasis was given on comparing and testing the performance of the intended model with other models on a public mammogram dataset containing both malignant and benign cases; the optimized SVM model provided an accuracy of 92%. 5%, sensitivity of 91. 0%, specificity of 93. 74 (fig 5) and 8%, and an AUC-ROC of 0. 95. This study shows that the method is efficient in discriminating between the malignant and benign first axillary lymph node tissue. The developed CAD system was found to possess the prospect of becoming a valuable support system for radiologists in the early detection of breast cancer. Keywords: Breast cancer; Mammograms; Support Vector Machines; Histogram of Oriented Gradient Descent; computer-aided diagnosis.

关键词： Support vector machines Solid modeling Histograms Accuracy Sensitivity Mammography Breast cancer Vectors Telecommunication computing Testing

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A Software Defect Prediction Method Based on Learnable Three-Line Hybrid Feature Fusion

SSRN

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SSRN 2023年

作者： Tang, Yu Dai, Qi Du, Ye Chen, Lifang Niu, Xuanwen School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Automation College of Information Science and Engineering China University of Petroleum-Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China

Software defect prediction plays a crucial role in ensuring the security and quality of software systems. However, it faces challenges posed by high-dimensional features present in software defect datasets and the limited effectiveness of traditional nonlinear dimensionality reduction methods in extracting essential feature information. To address these issues, we propose a novel approach called learnable three-line hybrid feature fusion (LTHFFA), which incorporates the principle of three-line hybrid breeding into feature fusion for the first time. In this method, three distinct dimensionality reduction techniques are initially employed to obtain three separate sets of features. Subsequently, a learnable weight factor feature fusion method is proposed to facilitate automatic learning and dynamic adjustment of feature weights. By integrating the three feature sets based on the principle of three-line hybrid breeding, we derive learnable three-line hybrid fusion features. These features are then utilized in the context of software defect prediction. Experimental results demonstrate the superior performance of LTHFFA compared to nine other dimensionality reduction methods across seventeen publicly available software defect datasets. LTHFFA exhibits the ability to effectively integrate multiple feature sets, reduce feature redundancy, and enhance predictive accuracy. Moreover, statistical analysis using Friedman ranking and Holm's post-hoc test confirms the significant advantage of LTHFFA over alternative dimensionality reduction methods. © 2023, The Authors. All rights reserved.

关键词： Defects

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Hybridizing deep neural network for genes expression classification using histone modification 3

Hybridizing deep neural network for genes expression classif...

引用

3rd Smart Cities Symposium, SCS 2020

作者： Nair, Rajit Bhagat, Amit Shukla, Piyush Kumar Dutta, Pushan Kumar SoET Jagran Lakecity University Bhopal India Department of Computer Application MANIT Bhopal India Department of Computer Science and Engineering University Institute of Technology RGPV M.P. Bhopal India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Kolkata Kolkata India

ISBN: (纸本)9781839535635

Genes expression datasets are considered to be high dimensional dataset. It is very much needed that before predicting the genes expression dimensionality reduction must be performed. Machine learning algorithms fail to analyze genes expression due to its high dimensionality behaviour. These algorithms have to perform feature selection or feature extraction before classification. To overcome this dimensionality reduction approach deep learning algorithms are implemented which has shown improved performance. This paper is also based on the approach of deep learning algorithm which is based on a classification approach using hybridization of a deep neural network named Hybrid Deep Neural Network (HDNN) in which Convolutional Neural Networks are combined with Recurrent Neural Networks to perform prediction. Parameters like accuracy, AUC, precision, and F1score have shown improved performance than other state of art algorithms with 85.5%, 0.84, 0.81, and 0.79 respectively. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Deep neural networks

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PoW-Based Sybil Attack Resistant Model for P2P Reputation Systems 2nd

PoW-Based Sybil Attack Resistant Model for P2P Reputation Sy...

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2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020

作者： Li, Biaoqi Fu, Xiaodong Yue, Kun Liu, Li Liu, Lijun Feng, Yong Yunnan Provincial Key Laboratory of Computer Technology Application Kunming650500 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China School of Information Science and Engineering Yunnan University Kunming650091 China

ISBN: (纸本)9789811592126

Peer-to-peer and other distributed systems are known particularly vulnerable to Sybil attack, so is P2P reputation systems. Reputation helps users to make better decisions in P2P systems, but Sybil attacker can obtain multiple identities and pretends to be multiple. It can control more than "one vote" to let others’ reputation change just as the attacker wants. This paper presents a Sybil attacker resistant model for P2P reputation systems by introducing multiple rounds of PoW (Proof-of-Work) verification and dynamic difficulty adjustment. A savvy attacker usually doesn’t carry out attacks without positive returns, so we minimize the expected profit of attackers through the using of puzzles. The costs and benefits are quantified as effectiveness in our model. We explore the effectiveness in two types of comparison, one is in dynamic or static difficulty adjustment, the other is executing once or multiple rounds of verification, and show the effectiveness of our model both analytically and experimentally. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Peer to peer networks

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PlaneSAM: Multimodal Plane Instance Segmentation Using the Segment Anything Model

arXiv

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arXiv 2024年

作者： Deng, Zhongchen Yang, Zhechen Chen, Chi Zeng, Cheng Meng, Yan Yang, Bisheng School of Computer Science and Information Engineering Hubei University Hubei Wuhan430062 China School of Artificial Intelligence Hubei University Hubei Wuhan430062 China Ministry of Education Hubei Wuhan430062 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Hubei Wuhan430072 China Engineering Research Center of Space-Time Data Capturing and Smart Application The Ministry of Education of P.R.C. Hubei Wuhan430072 China Institute of Geospatial Intelligence Wuhan University Hubei Wuhan430072 China

Plane instance segmentation from RGB-D data is a crucial research topic for many downstream tasks, such as indoor 3D reconstruction. However, most existing deep-learning-based methods utilize only information within the RGB bands, neglecting the important role of the depth band in plane instance segmentation. Based on EfficientSAM, a fast version of the Segment Anything Model (SAM), we propose a plane instance segmentation network called PlaneSAM, which can fully integrate the information of the RGB bands (spectral bands) and the D band (geometric band), thereby improving the effectiveness of plane instance segmentation in a multimodal manner. Specifically, we use a dual-complexity backbone, with primarily the simpler branch learning D-band features and primarily the more complex branch learning RGB-band features. Consequently, the backbone can effectively learn D-band feature representations even when D-band training data is limited in scale, retain the powerful RGB-band feature representations of EfficientSAM, and allow the original backbone branch to be fine-tuned for the current task. To enhance the adaptability of our PlaneSAM to the RGB-D domain, we pretrain our dual-complexity backbone using the segment anything task on large-scale RGB-D data through a self-supervised pretraining strategy based on imperfect pseudo-labels. To support the segmentation of large planes, we optimize the loss function combination ratio of EfficientSAM. In addition, Faster R-CNN is used as a plane detector, and its predicted bounding boxes are fed into our dual-complexity network as prompts, thereby enabling fully automatic plane instance segmentation. Experimental results show that the proposed PlaneSAM sets a new state-of-the-art (SOTA) performance on the ScanNet dataset, and outperforms previous SOTA approaches in zero-shot transfer on the 2D-3D-S, Matterport3D, and ICL-NUIM RGB-D datasets, while only incurring a 10% increase in computational overhead compared to EfficientSAM. Ou

关键词： 3D reconstruction

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Multi-stage image denoising with the wavelet transform

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Bob Zhang, Yanning Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Department of Computer and Information Science University of Macau 999078 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty. In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a dynamic convolution to dynamically adjust parameters of several convolutions for making a tradeoff between denoising performance and computational costs. WEB uses a combination of signal processing technique (i.e., wavelet transformation) and discriminative learning to suppress noise for recovering more detailed information in image denoising. To further remove redundant features, RB is used to refine obtained features for improving denoising effects and reconstruct clean images via improved residual dense architectures. Experimental results show that the proposed MWDCNN outperforms some popular denoising methods in terms of quantitative and qualitative analysis. Codes are available at https://***/hellloxiaotian/MWDCNN. © 2022, CC BY-NC-ND.

关键词： Wavelet transforms

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Sentiment Analysis of Amazon Customer Reviews Using Bi-Lstm, Bi-Gru, and Distilbert for the Purpose of Opinion Miningsentiment Analysis of Amazon Customer Reviews Using Bi-Lstm, Bi-Gru, and Distilbert for the Purpose of Opinion Mining

SSRN

引用

SSRN 2023年

作者： Tebyani, Sahand Jazayeri, Kian Ahadi, Seyedeh Aridis Çelebi, Erbuğ Jahandar, Farzad Department of Management Information Systems School of Applied Sciences Cyprus International University Haspolat Turkish Republic of Northern Cyprus Cyprus Department of Engineering Artificial Intelligence Application and Research Center Cyprus International University Haspolat Turkish Republic of Northern Cyprus Cyprus Khoury College of Computer Science Northeastern University United States Cyprus International University Cyprus

Recurrent Neural Networks (RNNs) have long had a dominant position in the fields of sentiment analysis and Natural Language Processing (NLP). This research gives a thorough comparative analysis of two unique models for opinion mining: Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). Notably, these models have a GloVe-based tokenizer installed to improve their verbal comprehension. We use the Amazon reviews dataset, concentrating on mobile devices and accessories, a rich source of varied user opinions, to assess their effectiveness. We compare their performance to that of the powerful large language model DistilBERT in addition to bespoke models. Our study goes into the complex technique of sentiment analysis to unearth subtle insights. Performance assessments cover a wide range of measures and factors and provide a comprehensive understanding of the models' capabilities. We also contrast how well these models extract highly relevant terms, demonstrating how well they can extract insightful information from user-generated content. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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