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检索条件"任意字段=4th International Conference Pattern Recognition in Bioinformatics"

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A Visual Method for Ship Close Encounter pattern recognition based on Fuzzy theory and Big Data Intelligence 20

A Visual Method for Ship Close Encounter Pattern Recognition...

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Proceedings of the 4th international conference on Big Data Research

作者： Liangbin Zhao Xiuju Fu A*STAR Singapore IHPC A*star Singapore

ISBN: (纸本)9781450387750

As an important application of big data technology in the maritime field, big data driven visualization of ship encounter patterns helps to intuitively understand the risk situation in the water traffic. However traditional methods based on fixed thresholds do not consider the fuzziness of classification on ship encounter situations. We proposed a visual method to visualize the risk situations caused by the interaction between vessel traffic flows in more detail based on fuzzy theory and big data intelligence on large scale AIS data. A case study is conducted to verify the applicability based on the AIS data from Singapore Strait. Visualization results of density in grids show that the proposed method can effectively reflect the ship encounter patterns, which are consistent with the real situation and can show more valuable details for the safety assessment of water traffic.

关键词： Ship encounter Fuzzy theory Visualization AIS data

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Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario 20

Face Anti-Spoofing by Learning Polarization Cues in a Real-W...

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Proceedings of the 4th international conference on Advances in Image Processing

作者： Yu Tian Kunbo Zhang Leyuan Wang Zhenan Sun Institute of AutomationChinese Academy of Sciences and Tianjin Academy for Intelligent Recognition Technologies China Chinese Academy of Sciences and Tianjin Academy for Intelligent Recognition Technologies China Institute of AutomationChinese Academy of Sciences and University of Chinese Academy of Sciences China Institute of Automation Chinese Academy of Sciences and National Laboratory of Pattern Recognition China

ISBN: (纸本)9781450388368

Face anti-spoofing is the key to preventing security breaches in biometric recognition applications. Existing face anti-spoofing approaches usually well recognize the spoofing attacks when testing in particular datasets. But the performance drops drastically when it comes to the actual scene. In this paper, we try to boost the generalizability capability by learning the polarization features of human faces in real-time. A human face anti-spoofing method suitable for real scenario has been proposed, which resists spoofing attacks by automatically learning the physical characteristics of polarized biometric images. A computational framework is developed to extract and classify the unique face polarized features using convolutional neural networks and SVM together. Extensive experiments demonstrate the adv antages of our real-time polarized face anti-spoofing (PAAS) technique to counter diverse face spoofing attacks (print, replay, mask) in uncontrolled indoor and outdoor conditions after learning the polarized face information of 108 people. A four-directional polarized face image dataset (CASIA-DOLP) is released to inspire future applications within biometric anti-spoofing field.

关键词： Polarization Imaging Biometrics Face Anti-spoofing

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Metagenomic Face: Applying Machine Learning to Facial Microbiome Data for pattern recognition and Disease Classification

Metagenomic Face: Applying Machine Learning to Facial Microb...

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Intelligent Technologies (CONIT), international conference on

作者： M S Guru Prasad Raghavendra M Devadas Vani Hiremani Afsar Jahan J Praveen Gujjar Aryan Dahiya Department of Computer Science and Engg Graphic Era (Deemed to be University) Dehradun India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Computer Applications Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector Machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector Machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework’s efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis.

关键词： Support vector machines Measurement Logistic regression Metagenomics Microorganisms Accuracy Machine learning algorithms

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Real-Time Speech Signal Classification for Emotion Detection Using Deep Belief Convolutional Neural Network with Multi-Head Self-Attention

Real-Time Speech Signal Classification for Emotion Detection...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Haider Mohmmed Alabdeli D. Jayaprakash Gudapalli Karuna Mohan M K. Sarathy Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of IT Sona College of Technology Salem India Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of ISE Nitte Meenakshi institute of technology Bangalore India Department of EEE New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent years, the recognition of human emotions from speech signals has developed a significant attention, determined by the rising demand for affective human and computer interaction. However, the emotion detection from speech signals is a challenging task; hence the existing Convolutional Neural Network (CNN) offered a promising solution for speech emotion recognition. But, it has led to the need of low-latency processing, variability, robust to noise, and efficient computation. Hence, this research aims to develop a real-time speech signal classification system for emotion recognition using Deep Belief Convolutional Neural Network (DBCNN) with Multi-Head Self-Attention with Contextual Fusion Mechanism (MHSA-CFM). this process begins with the collection of input signals and preprocessed with Neural Stochastic Differential Equations (NSDE), as it utilizes stochastic differential equations to improve the flexibility. then, 3D Log-Mel Spectrogram is utilized for feature extraction, where it represents audio signals as 3D visual representation of time, frequency, and amplitude. Finally, DBCNN-MHSA-CFM is employed to classify the emotions according to speech signals. From the results, the proposed DBCNN-MHSA-CFM achieved outperforming results in terms of accuracy with 93.24% when compared to the existing Gaussian Mixture Modeling (GMM) model.

关键词： Emotion recognition Visualization Time-frequency analysis three-dimensional displays Accuracy Stochastic processes pattern classification Speech recognition Feature extraction Convolutional neural networks

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A SVM Approach for Vessel Fire Detection Based on Image Processing

A SVM Approach for Vessel Fire Detection Based on Image Proc...

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4th international conference on Modelling,Identification and Control(ICMIC 2012)

作者： Xuanfang Yang, Jialin Wang, Shizhao He China. with the college of Electronic and Information Engineering, Naval university of Engineering, Wuhan, China

Based on the features such as high convergence rate and global optimization of Support Vector Machine (SVM) which follows structure risk minimization principle, a method of fire detection is proposed, in which the shape of bright areas are analyzed by SVM and results are produced. After collecting images of fire and interference source under different conditions, datas of shape features are extracted. Many of them are used as training set and delivered to SVM;the other data are used as test set for pattern recognition. Fire experiments show that trained SVM with RBF kernel and SMO algorithm can recognize images with 94.32% accuracy rate.

关键词： SVM RBF Kernel fire detection image processing

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Comparison between PSD, FFT& Wavelet Transform methods for classification of Electromyography (EMG) Signals

Comparison between PSD, FFT& Wavelet Transform methods for c...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Nitin Mishra Abhishek Gupta Om Asati Nikhil Kushwaha Vijay Kumar Gupta Prabin Kumar Padhy Department of Electronics & Communication Engineering PDPM IIITDM Jabalpur MP India Department of Mechanical Engineering PDPMIIITDM Jabalpur MP India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Electromyography (EMG) signals are ending up being dynamically huge in various objectives together with medical, biological, orthotic, automation, and recuperation gadget, coordinated efforts, to say the very least. In any case, boisterous EMG signals are the huge impediments to be vanquished to achieve additionally created execution in the above applications. Distinguishing proof, taking care of, and portrayal assessment in electromyography (EMG) is really alluring on the grounds that it permits a more normalized and definite assessment of the neurophysiological, habitational, and assistive mechanical disclosures. EMG signals procured from muscles require progressed strategies for recognition, decay, handling, and arrangement. there are so many methods that have been invented for classification of these signals earlier but still it is a challenging task among us to find out best method for that. the motivation behind this manuscript is to delineate the different procedures and calculations for EMG signal investigation to give productive and powerful approaches to figuring out the sign and its temperament.

关键词： Wavelet transforms Industries pattern classification Organizations Muscles Electromyography Real-time systems

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Performance Evaluation Of Machine Learning Algorithms For Rainfall Prediction Using Dimensionality Reduction Techniques

Performance Evaluation Of Machine Learning Algorithms For Ra...

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international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Sapna Kumari Muhammad Owais Raza Arsha Kumari Dept. of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Dept. of Information and Computing University of Sufism and Modern Sciences Bhitshah Pakistan Dept. of Electronics Engineering Mehran University of Engineering & Technology Jamshoro Pakistan

ISBN: (数字)9798350335316

ISBN: (纸本)9798350335323

In the last few decades, tremendous change is observed in rainfall patterns which are majorly influenced by two major factors 1) climate change and 2) CO2 emission. Erratic rainfall patterns caused catastrophic effects on agriculture and human life in developing countries like Pakistan, where major economic growth is largely dependent on agriculture. the main objective of this study is to evaluate a performance different Machine learning algorithms for forecasting rainfall patterns using dimensionality reduction techniques on climate change indicators. For this purpose rainfall data was collected for Pakistan. Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms were used for feature selection and the evolution of models was performed using Root Mean Square error (RMSE), Root Absolute Error (RAE), and Coefficient of determination metrics. Results show that features obtained using the Pearson correlation produced the least error and Bayesian linear regression performed with the highest accuracy followed by Neural Network regression.

关键词： Climate change Rain pattern recognition Predictive models Machine learning Neural networks

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Real-Time Prediction of Patient Recovery Time using AI-driven Optimization Techniques in Healthcare

Real-Time Prediction of Patient Recovery Time using AI-drive...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Sujeet Kumar Sachin Kumar Gupta Madhumita Papita Chaudhary Sumit Kumar GL Bajaj Institute of Management Greater Noida India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

this study was designed to construct a real-time prediction model for the recovery of patients in hospitals through AI-based optimization algorithms. the studies employed electronic health records for data collection and artificial intelligence algorithms, including decision trees, random forest, and support vector regression, to develop the prediction model. the model was subsequently trained and tested on a patient record dataset to assess the solution's accuracy and value. Study results: the study showed that, on average, 85% of patient recovery time could be predicted in real time via the developed model. this could have considerable connotations for healthcare providers regarding how to treat and prioritize patients best. In addition, the abstract points to potential advantages of using AI-powered optimization of healthcare practices, such as better efficiency in a system, more minor costs, and higher quality patient care.) It also presents further exploration and model enhancement areas, including additional patient information or more advanced AI models.

关键词： Support vector machines Medical services Predictive models Prediction algorithms Real-time systems Planning pattern recognition Resource management Optimization Random forests

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Prediction of the IoT Security Situation Utilizing Hybrid Convolutional Neural Network and Long-Short Term Memory

Prediction of the IoT Security Situation Utilizing Hybrid Co...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Zonghui Guo School of Integrated Circuits Shandong College of Electronic Technology Jinan China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

the joint network of interconnected devices is known as Internet of things (IoT), that devices are capable to decide without human intervention and all the electric devices that are collected to connect with internet in order to share the data. So the advancement of these have increased popularity tremendously over past few years. this has been achieved by interconnecting the various functionalities with the heterogeneous physical devices. they have drastically increased the human lives in order to enhance their day- to-day tasks. this increases the risk of security of the data and privacy of the users. In order to secure the data from the threats and cyber-attacks a Hybrid Convolutional Neural Network and Long-Short Term Memory (Hybrid CNN-LSTM) is proposed in order to predict the IoT security situation. For preprocessing these methods are employed elimination of the unimportant features, random selection, and the duplication removal. then, the feature selection of done by utilizing the Decision Tree (DT) and Recursive Feature Elimination (RFE). Finally, the proposed Hybrid CNN-LSTM is utilized as a classifier. the results provide the how the efficiency of proposed Hybrid CNN-LSTM in terms of accuracy, precision, recall, and f1-score, when the existing methods are compared with the proposed method.

关键词： Wireless communication Training Accuracy Predictive models Feature extraction pattern recognition Security Convolutional neural networks Internet of things Optimization

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Knowledge-Base Optimization to Reduce the Response Time of Bangla Chatbot

Knowledge-Base Optimization to Reduce the Response Time of B...

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international conference on Informatics, Electronics & Vision (ICIEV)

作者： Md. Kowsher Anik Tahabilder Md. Zahidul Islam Sanjid Nusrat Jahan Prottasha Md. Murad Hossain Sarker Noakhali Science and Technology University Noakhali Bangladesh School of Engineering + Technology Western Carolina University Cullowhee NC USA BRAC University Dhaka Bangladesh Daffodil International University Dhaka Bangladesh Comilla University Comilla Bangladesh

ISBN: (数字)9781728193311

ISBN: (纸本)9781728193328

Chatbots have been very popular in recent years for being able to serve as a customer representative, a language learner and so forth. Long short-term memory abbreviated as LSTM is a ubiquitous artificial recurrent neural network that is frequently being used for the chatbot. Nevertheless, if a user makes the line break of sequence, then it is rare to inform the right information without the impact of the previous sequence. As a result, in case of a help desk chatbot, LSTM is not the best option for taking steps of the right information. On the other hand, mathematical and statistical procedures are prominently useful for providing the proper knowledge without having back the impact of sequence. Still, it takes more execution time to respond. the goal of this paper is to present the optimal chatbot for the lowest execution time and three mathematical and statistical strategies for Bangla Intelligence chatbot in light of information obtained from Noakhali Science and Technology University (NSTU). As the procedures, we have followed cosine similarity, Jaccard similarity, and Naive Bayes classifier. To reduce the response time, we decorated the whole path into a 3-depth tree, such as a question, topic, and answer. We have compared the performance of the selected strategies where the best accuracy was 93.22% using the cosine similarity. Contribution-this paper presents techniques to reduce the response time of statistical and mathematical Bangla Chatbot.

关键词： Chatbot Time factors Training Optimization Mathematical model Text processing Statistical analysis

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