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Purity detection of goat milk based on electronic tongue and improved artificial fish swarm optimized extreme learning machine 6th

Purity detection of goat milk based on electronic tongue and...

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6th international-Federation-of-Automatic-Control (IFAC) conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)

作者： Han, Hui Wang, Zhiqiang Li, Caihong Ma, Zeliang Yang, Zhengwei Ma, Xiyuan Shandong Univ Technol Sch Comp Sci & Technol Zibo 255049 Peoples R China Lianyungang Tech Coll Coll Informat Engn Lianyungang 222000 Peoples R China Korea Univ Dept Comp Sci & Technol Seoul 02841 South Korea

the nutritional value of goat milk is higher than that of cow milk, which is scarce and expensive. therefore, many bad credit enterprises mix cow's milk into goat milk and sell it as pure goat milk, which infringes on the rights and interests of consumers. the adulteration of dairy products has a long history. Many techniques have been applied for the detection of dairy products' adulteration, but no obvious results have been achieved. the electronic tongue system, based on a virtual instrument, can provide fast detection of goat milk's quality. the qualitative identification of different purifies of goat milk by kernel principal component analysis (KPCA) has achieved 100% accuracy. By comparing the four algorithms of the optimal extreme learning machine (ELM), it was determined that the quantitative model for the prediction of adulterated goat milk, established by the improved artificial fish swarm optimized ELM method, had high prediction accuracy. the prediction set coefficient R-2 was 0.998, the average absolute error (MAE) was 0.083, and the root mean square error (MSE) was 0.011. According to its unique advantages, the electronic tongue provides a new idea and method for the detection of food adulteration. As a modern intelligent sensory instrument, the electronic tongue has great potential for brand identification as well as the identification of goat milk and goat milk powder adulteration. (C) 2019, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Goat milk quality detection Electronic tongue system pattern recognition Improved artificial fish swarm optimized extreme learning machine Adulteration

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U-stroke pattern modeling for end user identity verification through ubiquitous input device

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2015年 9339卷 219-230页

作者： Bhattasali, Tapalina Chaki, Nabendu Saeed, Khalid Chaki, Rituparna Department of Computer Science and Engineering University of Calcutta Kolkata India Bialystok University of Technology Bialystok Poland A.K.Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (纸本)9783319243689

Identity verification on ubiquitous input devices is a major concern to validate end-users, because of mobility of the devices. User device interaction (UDI) is capable to capture end-users’ behavioral nature from their device usage pattern. the primary goal of this paper is to collect heterogeneous parameters of usage patterns from any device and build personal profile with good-recognition capability. this work mainly focuses on finding multiple features captured from the usage of smart devices;so that parameters could be used to compose hybrid profile to verify end-users accurately. In this paper, U-Stroke modeling is proposed to capture behavioral data mainly from smart input devices in ubiquitous environment. In addition to this, concept of CCDA (capture, checking, decision, and action) model is proposed to process U-Stroke data efficiently to verify enduser’s identity. this proposal can draw attention of many researchers working on this domain to extend their research towards this direction. © IFIP international Federation for Information Processing 2015.

关键词： Touch screens

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Transformer Partial Discharge recognition Based on Phase-Preserving Data Augmentation

Transformer Partial Discharge Recognition Based on Phase-Pre...

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Energy, Power and Grid (ICEPG), international conference on

作者： Shifang He Mingtao Yu Zongzhen Yang Binghui Lei Department of China Yangtze Power Co.Ltd. Yichang Xiling Road 1 Hubei China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

the identification of transformer Partial Discharge (PD) is crucial for the safe and stable operation of modern power systems. Traditional PD identification methods rely on manual feature extraction, which has limited generalization capability. Moreover, when applying deep learning algorithms to Phase-Resolved Partial Discharge (PRPD) patterns, conventional data augmentation techniques may disrupt the phase distribution, leading to the loss of critical features and affecting identification accuracy. To address this issue, this paper proposes a PD identification method based on phase-preserving data augmentation techniques. By incorporating Gaussian noise addition, phase shift processing, and segmented perturbation processing, the proposed method effectively retains the phase information in PRPD patterns, avoiding the loss of critical features during data augmentation. Experimental results show that the proposed method significantly outperforms traditional methods in terms of accuracy and stability, achieving a maximum accuracy of 96.39%, demonstrating great potential for practical engineering applications.

关键词： Partial discharges Accuracy Perturbation methods Gaussian noise Power system stability Data augmentation Data models Stability analysis Robustness pattern recognition

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A secret sharing scheme based on game theory and BP neural network*

A secret sharing scheme based on game theory and BP neural n...

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Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys), IEEE international conference on

作者： Qiuxian Li Quanxing Zhou Qixin Pan Lingyun Wang Hongfa Ding College of Big Data Engineering Kaili University Kaili China College of Information Guizhou University of Finance and Economics Guiyang China

ISBN: (数字)9781728176512

ISBN: (纸本)9781728176529

In order to solve the problem of low efficiency of traditional secret sharing communication, we design a new rational secret sharing scheme based on BP neural network under the framework of game theory. We first propose an extended system game model for rational secret sharing, and define the set of participants, information set, feasible strategies, and utility functions of the secret sharing game. Secondly, using the characteristics of pattern recognition and classification of BP neural network, the secret input and output training patterns are correlated to realize nonlinear mapping in the neural network, thereby identifying the sub-secrets owned by honest and rational participants and recovering the correct secret. We design a communication channel by using the random function of bilinear pair on elliptic curve to ensure interactive communication among participants and reduce the communication complexity of the solution. Finally, the analysis proves the effectiveness and security of this scheme. By comparing with the existing scheme, this scheme not only has higher communication efficiency, but also meets its security requirements.

关键词： Training Neural networks Games Resists pattern recognition Cryptography Game theory

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OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features

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JOURNAL OF bioinformatics AND COMPUTATIONAL BIOLOGY 2016年第1期14卷 1650006-1650006页

作者： Pian, Cong Zhang, Jin Chen, Yuan-Yuan Chen, Zhi Li, Qin Li, Qiang Zhang, Liang-Yun Nanjing Agr Univ Coll Sci Nanjing 210095 Jiangsu Peoples R China

MicroRNAs (miRNAs) are a set of short (21-24 nt) non-coding RNAs that play significant regulatory roles in the cells. Triplet-SVM-classifier and MiPred (random forest, RF) can identify the real pre-miRNAs from other hairpin sequences with similar stem-loop (pseudo pre-miRNAs). However, the 32-dimensional local contiguous structure-sequence can induce a great information redundancy. therefore, it is essential to develop a method to reduce the dimension of feature space. In this paper, we propose optimal features of local contiguous structure-sequences (OP-Triplet). these features can avoid the information redundancy effectively and decrease the dimension of the feature vector from 32 to 8. Meanwhile, a hybrid feature can be formed by combining minimum free energy (MFE) and structural diversity. We also introduce a neural network algorithm called extreme learning machine (ELM). the results show that the specificity (S-p) and sensitivity (S-n) of our method are 92.4% and 91.0%, respectively. Compared with Triplet-SVM-classifier, the total accuracy (ACC) of our ELM method increases by 5%. Compared with MiPred (RF) and miRANN, the total accuracy (ACC) of our ELM method increases nearly by 2%. What is more, our method commendably reduces the dimension of the feature space and the training time.

关键词： RNA germ cells pattern recognition algorithm

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Doubly Regularized Least Squares Twin Extreme Learning Machine for pattern Classification 23

Doubly Regularized Least Squares Twin Extreme Learning Machi...

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Proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Qing Wu Ming Yu Lu Chen School of Automation Xi' an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Least squares twin extreme learning machine (LSTELM) possesses better generalization ability than the standard extreme learning machine (ELM) by finding two nonparallel hyperplanes in ELM random feature space. However, LSTELM is not sparse in nature and does not take into account the intrinsic geometric information of the data. To overcome the above, this paper proposes one classification method based on LSTELM, namely doubly regularized least squares twin extreme learning machine (DRLSTELM). DRLSTELM uses L2,1-norm regularization and manifold regularization methods to improve the classification performance. Furthermore, in its nonlinear case, a hybrid kernel function is introduced to improve the generalization and learning ability of the model. To evaluate the classification performance of the proposed algorithm, experiments were conducted on the NDC datasets and the benchmark datasets respectively. the results show that the classification effect of DRLSTELM is significantly better than other state-of-the-art methods.

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Enhancing Typing Dynamics Emotion recognition: A Multi-Class XGBoost Approach for Accurate Sentiment Detection

Enhancing Typing Dynamics Emotion Recognition: A Multi-Class...

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Advancements in Computing (ICAC), international conference on

作者： Sudesh Bandara tharindu Dasath Senula Nanayakkara Samadhi Rathnayake thusithanjana thilakarthna Faculty of Computing Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

this research paper aims to enhance the accuracy and efficiency of emotion recognition through keystroke dynamics by utilizing a multi-class XGBoost model. the study addresses the limitations observed in previous models, such as Support Vector Machines and Logistic Regression, which struggled with the complex, high-dimensional nature of keystroke data. the primary objective was to develop a robust model capable of capturing the subtle variations in typing behavior associated with different emotional states. To achieve this, a comprehensive approach was adopted, involving the collection of detailed keystroke datasets, advanced feature engineering, and systematic hyperparameter tuning. the XGBoost model was chosen for its superior ability to manage complex interactions within large datasets. through rigorous evaluation, the XGBoost model outperformed traditional models in accuracy, precision, and recall, demonstrating significant improvements in emotion classification tasks. the findings of this study reveal that XGBoost, when combined with meticulous feature engineering and hyperparameter optimization, offers a substantial advancement in the field of emotion recognition via keystroke dynamics. the research not only confirms the effectiveness of XGBoost in this domain but also provides new insights into optimizing machine learning models for emotion detection. these results have broad implications for the development of more emotionally aware computing systems, potentially improving user interaction across various applications.

关键词： Support vector machines Emotion recognition Logistic regression Adaptation models Accuracy Systematics Computational modeling Keystroke dynamics pattern recognition Tuning

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Face Forgery Detection Algorithm Based on Improved MobileViT Network

Face Forgery Detection Algorithm Based on Improved MobileViT...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Tiantian Wang Xiaoqi Lu School of Information Engineering Inner Mongolia University of Science and Technology Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China Institute of Information Engineering Inner Mongolia University of Technology Hohhot China

DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. through analysis of existing face forgery techniques, it is found that most generation techniques rely on random noise distribution, and global information will be lost after up sampling. therefore, we propose a deepfake detection algorithm based on improved MobileViT, which uses CNN local space biasing and the global space representation of the Transformer network to learn the local features and global representation of forged faces, respectively. Coordinate attention is introduced to obtain directional perception and position sensitive information, making the model locate synthetic traces of fake faces better and fusion local and global representation more effectively. For the improved generalization of the model, with the GELU activation function to solve the problem of neuron death. Our model achieved 96.2% on FF++(C23) datasets, and 93.7%,94.1%,96.3%,87.9% on DF, F2F, FS, and NT datasets, respectively. Comparing with previous methods, our model has shown detection robustness and better generalization.

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Spammer Detection on Weibo Social Network

Spammer Detection on Weibo Social Network

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IEEE international conference on Cloud Computing Technology and Science

作者： Zhipeng Zeng Xianghan Zheng Guolong Chen Yuanlong Yu Coll. of Math. & Comput. Sci. Fuzhou Univ. Fuzhou China

ISBN: (纸本)9781479940929

Social network has become a very popular way for internet users to communicate and interact online. Users spend a great deal of time on famous social networks (e.g. Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting their messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behaviors (e.g. post messages containing commercial topics or URLs, following a larger amount of users, etc.), leading to great inconvenience on normal users' social activities. In this paper, a supervised machine learning based spammer filtering method is proposed. We first collected a dataset from Sina Weibo that includes 30,116 users and more than 16 million messages;then, construct a labeled dataset of users and manually classify users into spammers and non-spammers;after that, abstract a set of novel features from message content and users' social behavior, and apply into SVM based spammer classifier. Our experiments show that true positive rate of spammers and non-spammers could reach 99.1% and 99.9%.

关键词： Internet learning (artificial intelligence) pattern classification social networking (online) support vector machines unsolicited e-mail Internet users SVM based spammer classifier Weibo social network event discussion malicious behaviors message content message posting news reading normal user social activities spammer detection supervised machine learning based spammer filtering method user labeled dataset user social behavior Classification algorithms Crawlers Feature extraction Support vector machines Twitter Unsolicited electronic mail machine learning social network spam spammer support vector machine spammer Support Vector Network Unsolicited electronic mail Social Media Social Networks Crawlers Sorting algorithm Consumers pattern recognition Feature extraction Internets TWITTER

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Research on the Detection of Abnormal Cervical Cells Based on Improved YOLOv7

Research on the Detection of Abnormal Cervical Cells Based o...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Xuewei Wang Shuang Yuan Lidong Yang Dawei Niu Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. the algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. the experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively.

关键词： Accuracy Sensitivity Shape Prevention and mitigation Signal processing algorithms Signal processing Feature extraction Convolutional neural networks Detection algorithms Cervical cancer

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