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New Three-Dimensional Assessment Model and Optimization of Acoustic Positioning System

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Computers, Materials & Continua 2020年第8期64卷 1005-1023页

作者： Lin Zhao Xiaobo Chen Jianhua Cheng Lianhua Yu Chengcai Lv Jiuru Wang college of Automation Harbin Engineering UniversityHarbin150001China Department of Electrical and Electronic Engineering The University of MelbourneVictoria3010Australia college of Information and Communication Harbin Engineering UniversityHarbin150001China Institute of Deep-Sea Science and Engineering Chinese Academy of SciencesSanya572000China School of Information Science and Engineering Linyi UniversityLinyi276005China

This paper addresses the problem of assessing and optimizing the acoustic positioning system for underwater target localization with range *** present a new three-dimensional assessment model to evaluate the optimal geometric beacon formation whether meets user *** mathematical tractability,it is assumed that the measurements of the range between the target and beacons are corrupted with white Gaussian noise with variance,which is ***,the relationship between DOP parameters and positioning accuracy can be derived by adopting dilution of precision(DOP)parameters in the assessment *** addition,the optimal geometric beacon formation yielding the best performance can be achieved via minimizing the values of geometric dilution of precision(GDOP)in the case where the target position is known and ***,in order to ensure that the estimated positioning accuracy on the region of interest satisfies the precision required by the user,geometric positioning accuracy(GPA),horizontal positioning accuracy(HPA)and vertical positioning accuracy(VPA)are utilized to assess the optimal geometric beacon *** examples are designed to illustrate the exactness of the *** other work that only uses GDOP to optimize the formation and cannot assess the performance of the specified size,this new three-dimensional assessment model can evaluate the optimal geometric beacon formation for each dimension of any point in three-dimensional space,which can provide guidance to optimize the performance of each specified dimension.

关键词： Acoustic positioning system three-dimensional assessment model positioning accuracy DOP optimal configuration

Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

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Computers, Materials & Continua 2023年第4期75卷 1107-1117页

作者： Sumin Xie Ling Liu Guang Sun Bin Pan Lin Lang Peng Guo Chenzhou Vocational Technical college Chenzhou423000China Hunan University of Finance and Economics Changsha410205China University Malaysia Sabah Kota Kinabalu88999Malaysia

The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine ***,fraud detection methods based on conventional machine learning approaches suffer from several problems,including an excessively high number of network parameters,which decreases the efficiency and increases the difficulty of training the network,while simultaneously leading to network *** addition,the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained *** present work addresses these issues by proposing a convolutional neural network(CNN)framework for detecting ecommerce fraud,where network training is conducted using historical market transaction *** number of network parameters reduces via the local perception field and weight sharing inherent in the CNN *** addition,this deep learning framework enables the use of an algorithmiclevel approach to address dataset imbalance by focusing the CNN model on minority data *** proposed CNN model is trained and tested using a large public e-commerce service dataset from 2018,and the test results demonstrate that the model provides higher fraud prediction accuracy than existing state-of-the-art methods.

关键词： CNN model detection e-commerce fraud

A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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Computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

Machine learned interatomic potentials using random features

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npj Computational Materials 2022年第1期8卷 48-57页

作者： Gurjot Dhaliwal Prasanth B.Nair Chandra Veer Singh Department of Mechanical and Industrial Engineering University of Toronto5 King’s College RdTorontoM5S 3G8ONCanada Institute for Aerospace Studies University of Toronto4925 Dufferin StTorontoM3H 5T6ONCanada Department of Materials Science and Engineering University of Toronto184 College StTorontoM5S 3E4ONCanada

We present a method to model interatomic interactions such as energy and forces in a computationally efficient *** proposed model approximates the energy/forces using a linear combination of random features,thereby enabling fast parameter estimation by solving a linear least-squares *** discuss how random features based on stationary and non-stationary kernels can be used for energy approximation and provide results for three classes of materials,namely two-dimensional materials,metals and *** and energy predictions made using the proposed method are in close agreement with density functional theory calculations,with training time that is 96%lower than standard kernel *** Dynamics calculations using random features based interatomic potentials are shown to agree well with experimental and density functional theory *** frequencies as computed by random features based interatomic potentials are within 0.1%of the density functional theory ***,the proposed random features-based potential addresses scalability issues encountered in this class of machine learning problems.

关键词： random stationary interatomic

Large margin classification for combatingdisguise attacks on spam filters

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Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第3期13卷 187-195页

作者： Xi-chuan ZHOU Hai-bin SHEN Zhi-yong HUANG Guo-jun LI college of Communications Engineering Chongqing UniversityChongqing40044China Institute of VLSI Design Zhejiang UniversityHangzhou 310027China Chongqing Communication Institute Chongqing 400032China

This paper addresses the challenge of large margin classification for spare filtering in the presence of an adversary who disguises the spam mails to avoid being detected. In practice, the adversary may strategically add good words indicative of a legitimate message or remove bad words indicative of spam. We assume that the adversary could afford to modify a spam message only to a certain extent, without damaging its utility for the spammer. Under this assumption, we present a large margin approach for classification of spare messages that may be disguised. The proposed classifier is formulated as a second-order cone programming optimization. We performed a group of experiments using the TREC 2006 Spam Corpus. Results showed that the performance of the standard support vector machine （SVM） degrades rapidly when more words are injected or removed by the adversary, while the proposed approach is more stable under the disguise attack.

关键词： Large margin Spam filtering Second-order cone programming (SOCP) Adversarial classification

Roles of mushroom polysaccharides in chronic disease management

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Journal of Integrative Agriculture 2022年第7期21卷 1839-1866页

作者： ZHANG Shan LEI Lin ZHOU Yun YE Fa-yin ZHAO Guo-hua college of Food Science Southwest UniversityChongqing 400715P.R.China Chongqing Engineering Research Center of Regional Foods Chongqing 400716P.R.China

Chronic diseases have drawn much attention as the primary cause of death and disability. In exploring novel sideeffect-free agents against chronic diseases, significant efforts have been devoted to mushroom polysaccharides due to their diverse biological activities. This work reviewed the structural features, biological performances and molecular mechanisms of mushroom polysaccharides in managing cancers, diabetes mellitus and cardiovascular diseases. The potentials of mushroom polysaccharides against chronic diseases highly depend on their structural features, including monosaccharide composition, molecular weight, the type and configuration of glycosidic bonds, degree of branching, the type of substituent pattern and chain conformation. Regarding their working mechanisms, shared and diseasespecific pathways were found. The three chronic diseases shared the regulation of specific signalling pathways and the adjustment of gut microbiota. In addition, the roles of transcription factors, receptors, enzymes, hormones and other functional proteins involved in the molecular mechanisms of mushroom polysaccharides against chronic diseases are first elaborated herein. The present review describes the state of the art of mushroom polysaccharides in treating chronic diseases and addresses the perspectives, and will further promote research on this topic.

关键词： mushroom polysaccharide chronic diseases structural feature biological performance molecular mechanism

A Covert Communication Method Using Special Bitcoin addresses Generated by Vanitygen

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Computers, Materials & Continua 2020年第10期65卷 597-616页

作者： Lejun Zhang Zhijie Zhang Weizheng Wang Rasheed Waqas Chunhui Zhao Seokhoon Kim Huiling Chen college of Information Engineering Yangzhou UniversityYangzhou225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou362000China University of Aizu Aizu Wakamatsu9658580Japan college of Information and Communication Engineering Harbin Engineering UniversityHarbin150001China Department of Computer Software Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China

As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually *** blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance,which can effectively alleviate the disadvantages and problems of traditional covert ***,its combination with covert communication thus far has been mostly at the theoretical *** BLOCCE method,as an early result of the combination of blockchain and covert communication technology,has the problems of low information embedding efficiency,the use of too many Bitcoin addresses,low communication efficiency,and high *** present research improved on this method,designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed *** greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses *** the premise of ensuring the order,the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission ***,a more efficient and feasible method for the application of covert communication on the blockchain is *** addition,this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.

关键词： Covert communication blockchain Bitcoin address

Autism Spectrum Disorder:The Dilemma of Untimely Recognition,Intervention and Diagnostic Scales Obtainable at Indian Sub-continent

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Journal of Psychological Research 2020年第1期2卷 1-11页

作者： C.S.Kanimozhiselvi S.Poonguzhali D.Jayaprakash Kongu Engineering college PerunduraiErodeIndia

The increasing prevalence of Autism Spectrum Disorder makes it as considerable issue *** studies addresses the hot topic of Mirror Neuronal System(MNS)confers behind the ***,the cause is uncertain,Indian population prone to varied prenatal and postnatal factors of the *** parents and professional still be at the initial awareness phase of the *** of delay in identification and intervention while comparing with world standards due to various Indian socio-economic and socio-cultural *** availability of screening and diagnostic tools headed to relay on culturally irrelevant and expensive international *** funded research initiatives developed ISAA,INDT-ASD,CASI and AIIMS Modified INDT-ASD as culturally relevant indigenous tools and available on *** far,the tools have their own advantages and limitations,requires further research and *** to scarcity of trained professionals for a wide population range,home based parent-mediated therapies be the most preferred mode of ***,the therapeutic options vary with *** study aimed to ascertain the present Indian scenario,look upon the awareness about the condition,availability of screening and diagnostic facilities,the early identification and timely intervention *** addition,the study briefly confers the biological and clinical background of ASD.

关键词： DSM-V Neurobiology Genetics M-CHAT(R/F) CARS2 PASS Socio-cultural factors

Cooperative deterministic learning control for a group of homogeneous nonlinear uncertain robot manipulators

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Science China(Information Sciences) 2018年第11期61卷 67-85页

作者： Marwan ABDELATTI Chengzhi YUAN Wei ZENG Cong WANG Department of Mechanical Industrial and Systems Engineering University of Rhode Island School of Mathematics and Big Data Foshan University college of Automation Science and Engineering South China University of Technology School of Mechanical and Electrical Engineering Longyan University

This paper addresses the learning control problem for a group of robot manipulators with homogeneous nonlinear uncertain dynamics, where all the robots have an identical system structure but the reference signals to be tracked differ. The control objective is twofold: to track on reference trajectories and to learn/identify uncertain dynamics. For this purpose, deterministic learning theory is combined with consensus theory to find a common neural network(NN) approximation of the nonlinear uncertain dynamics for a multi-robot system. Specifically, we first present a control scheme called cooperative deterministic learning using adaptive NNs to enable the robotic agents to track their respective reference trajectories on one hand and to exchange their estimated NN weights online through networked communication on the other. As a result, a consensus about one common NN approximation for the nonlinear uncertain dynamics is achieved for all the agents. Thus, the trained distributed NNs have a better generalization capability than those obtained by existing techniques. By virtue of the convergence of partial NN weights to their ideal values under the proposed scheme, the cooperatively learned knowledge can be stored/represented by NNs with constant/converged weights, so that it can be used to improve the tracking control performance without re-adaptation. Numerical simulations of a team of two-degree-of-freedom robot manipulators were conducted to demonstrate the effectiveness of the proposed approach.

关键词： cooperative deterministic learning multi-robot manipulators neural networks adaptive control multi-agent systems