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Efficient pattern recognition using a new transformation distance 92

Efficient pattern recognition using a new transformation dis...

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Proceedings of the 5th international conference on Neural Information Processing Systems

作者： Patrice Simard Yann Le Cun John Denker AT&T Bell Laboratories Holmdel NJ

ISBN: (纸本)9781558602748

Memory-based classification algorithms such as radial basis functions or K-nearest neighbors typically rely on simple distances (Euclidean, dot product...), which are not particularly meaningful on pattern vectors. More complex, better suited distance measures are often expensive and rather ad-hoc (elastic matching, deformable templates). We propose a new distance measure which (a) can be made locally invariant to any set of transformations of the input and (b) can be computed efficiently. We tested the method on large handwritten character databases provided by the Post Office and the NIST. Using invariances with respect to translation, rotation, scaling, shearing and line thickness, the method consistently outperformed all other systems tested on the same databases.

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Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System

Enhancing Security with Machine Learning-based Finger-Vein B...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： G Prakash Babu Upendra Singh Aswal M. Sindhu Vijay Jagdish Upadhye Natrayan L Harshal Patil Department of Computer Science and Engineering Acharya Institute of Technology Bangalore Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Business Systems R.M.K. Engineering College Thiruvallur Tamil Nadu India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Waghodia Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. the study employs two feature extraction approaches, the Local Binary pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. the simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task.

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Supervised Machine Learning: Discriminative Learning Methods for pattern Classification using HELM

Supervised Machine Learning: Discriminative Learning Methods...

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international conference on Inventive Research in Computing Applications (ICIRCA)

作者： N. Shirisha Ajmeera Kiran A. Prashanthi MrKamjula Lakshmi Kanth Reddy MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Computer Science and Engineering Nalla Narasimha Reddy Education Society's group of Institutions Department of Artificial Intelligence Anurag University Hyderabad

the ability to forecast the data is significantly helped by machine learning's involvement. At the present time, data analysis is playing a very significant part in both the Information Technology (IT) industry and academic institutions. Numerous real-time applications, such as translation in real time, facial recognition, musical compositions, and so on, have been developed through the various initiatives that have been carried out up to this point. According to the standards that must be met by every data analyst, data must first be preprocessed before any predictions can be made on that data. Predictions must also be based exclusively on the preprocessed data. this work demonstrates the data pre-processing along with the prediction of a churn model. the model contains a set of records of various customers, including information regarding whether or not the client possesses credit cards and whether or not the consumer is an active member. this research works on the construction of a hybrid ensemble learning model (abbreviated HELM) to collect heterogeneous weak learners in order to anticipate such analyses. In order to work on the Churn Model classification, a variety of machine learning algorithms are integrated with one another. the findings revealed the efficiency of each machine learning algorithm that was used, in addition to HELM's operational effectiveness.

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Sleep Staging Based on Cardiopulmonary Coupling Features Derived from Single-Lead ECG Signal

Sleep Staging Based on Cardiopulmonary Coupling Features Der...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Ye Lin Yidan Dai School of Artificial Intelligence South China Normal University Foshan China School of Computer Science South China Normal University Foshan China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Sleep staging is an important tool for diagnosing various sleep disorders. However, polysomnography (PSG), which serves as the gold standard for sleep detection, is challenging to acquire in non-clinical settings. Electrocardiogram (ECG) signals are readily obtainable and have been utilized in sleep staging research. the majority of existing studies have not taken into account neighborhood information and cardiopulmonary coupling technology. Actually, medical experts usually consider about 30 minutes or 1 hour of contextual information when staging a segment of ECG, so neighborhood segments can provide auxiliary information. In addition, clinical studies have proved that cardiopulmonary coupling technology plays a key role in sleep staging research. In view of these, we propose a sleep staging method based on ECG, cardiopulmonary coupling technology and deep neural network. More specifically, we first utilize the CNN model for automatic feature extraction and fusion of cardiopulmonary coupling features and the original ECG. then, noises effects caused by neighborhood information are removed through an efficient channel attention (ECA) mechanism. Finally, sleep stages are determined for signal segments. To verify the robustness of the model, we evaluate it on four publicly available datasets and a private dataset. Experiments show that our method outperforms other benchmarks.

关键词： Couplings Attention mechanisms Noise Artificial neural networks Electrocardiography Feature extraction Robustness Sensors pattern recognition Monitoring

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A Method for Chinese Sarcasm Detection Based on Enhanced Cross-Entropy and Regularization

A Method for Chinese Sarcasm Detection Based on Enhanced Cro...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Hang Ren Jing Zhang Yongqiang A Nuo Qun Shengqiao Ni Jin Yang Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region School of Information Science and Technology Tibet University Lhasa China School of Information Science and Technology Tibet University Lhasa China School of Mechanical Engineering Sichuan University Chengdu China School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In the context of social media, the automatic identification of sarcastic discourse presents a complex challenge within the field of natural language processing. Due to the significant disparity between the literal and implied meanings of sarcastic statements, traditional text analysis algorithms struggle to accurately detect them. this paper introduces a method for detecting Chinese sarcastic comments on social media, which integrates an improved cross-entropy loss function with regularization techniques. the method employs a dynamic adjustment factor to optimize weighted cross-entropy loss function, calibrating the model's penalty intensity for high-confidence predictions, and enhances the consistency of the model's output through batch-averaged Kullback-Leibler divergence. Experiments were conducted using the Chinese-RoBERTa model as a baseline, validated on full data and small sample sizes (50%, 10%). the results demonstrate that, compared to the baseline model, the proposed method achieved accuracy improvements of 3.7%, 2.5%, and 2.3% across different data volumes, confirming its effectiveness and applicability in the detection of Chinese sarcastic discourse.

关键词： Visualization Text analysis Social networking (online) Machine learning Predictive models Prediction algorithms Data models Natural language processing pattern recognition Optimization

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Artificial Intelligence approaches for Predicting Hypertension Diseases: Open Challenges and Research Issues

Artificial Intelligence approaches for Predicting Hypertensi...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Simranjit Kaur Khushboo Bansal Yogesh Kumar Desh Bhagat University Mandi Gobindgarh Punjab Computer Science Engineering Pandit Deendayal Energy University Gandhinagar India

Despite improved therapies and decades of research over the last century, hypertension remained the world's leading avoidable cause of death. Machine learning (ML) is a subfield of artificial intelligence that integrates computer science, statistics, and decision theory to recognize complicated patterns in vast amounts of data. Because hypertension may induce a rapid rise in blood pressure, artificial intelligence approaches are used to anticipate the start of hypertension the study's main goal is to use artificial intelligence technologies like deep learning (DL) and machine learning (ML) to predict hypertension. this research will analyze the current state, challenges, and potential of adopting technologies to detect and predict hypertension diseases. thus, AI-integrated hypertension care has the ability to revolutionize clinical practice by introducing individualized approaches to prevention and treatment, such as the establishment of optimal and patient-specific blood pressure (BP) goals, and the development of interventions focusing on modifiable risk factors. Prior research has showed that machine learning has the ability to improve all areas of patient care, from research and development to everyday clinical practice and global health. the purpose of this paper is to illustrate the potential of artificial intelligence for treating hypertension by analyzing existing evidence.

关键词： Hypertension Deep learning Decision theory Medical treatment Focusing Pressure measurement pattern recognition

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Modeling and Analyzing the Manifolds of Cortical-Muscle During Card-Grabbing by Preferential Subspace Identification

Modeling and Analyzing the Manifolds of Cortical-Muscle Duri...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Jiyu Tan Yurong Li Jixiang Li Nan Zheng College of Electrical Engineering and Automation Fuzhou University Fuzhou China Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology Fuzhou University Fuzhou China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

the primary aim of this study is to construct a state-space model that reduces the complexity of the human sensorimotor control system and explore motion control theory based on manifolds. Low-dimensional neural and muscle manifolds, representing the population activity of the brain and muscles, quantify the sensorimotor function of the human cortex, revealing hidden information about how the brain controls movement. this study used EEG and EMG signals from 25 participants during a card grabbing task. Preferential subspace recognition (PSID) algorithm was used to construct a state space model of subjects to study the neural computational strategies for sensorimotor control in the preparation and execution phases of motion, as well as the differences between brain and muscle population activity. the results show that from the preparation stage to the execution stage, the population activity of cortical muscles follows the orthogonal neural computation strategy in the subspace. the degree of trajectory entanglement between neural manifolds and muscle manifolds is greater than that during exercise preparation. Moreover, neural manifolds are less entangled than muscle manifolds and show a more stable state in structure. these results show that the adopted approach effectively reveals the orthogonal neural mechanisms of brain control during movement, highlighting the differences in control output and feedback input in sensorimotor control. this offers a new perspective on how the brain controls complex movements.

关键词： Manifolds Computational modeling Muscles Aerospace electronics Control systems Brain modeling State-space methods Complexity theory Trajectory pattern recognition

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Classification Assistance Based Image Inpainting for BR code

Classification Assistance Based Image Inpainting for BR code

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Information Science, Parallel and Distributed Systems (ISPDS), international conference on

作者： Yuting Yang Jinfeng Li Naifeng Liang Man Li Xiangyun Cai Ziyao Zheng City College of Huizhou

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Recently, many image inpainting methods have achieved promising performance in recovering damaged natural images in various scenes. Different from natural images, blur-readable (BR) 2D barcode images have distinct structural characteristics, such as finder patterns (represent the version information) and data bits. Version information is crucial for decoding BR code. the problem of missing data bits can be solved by image inpainting methods and error correction codes. However, when the finder patterns are damaged, they may not be correctly repaired through the inpainting technology, resulting in the failure of barcode recognition. the limitation of existing image inpainting algorithms is that the version information of BR code cannot be obtained while generating repaired result. In this paper, we innovatively introduce the classification task into the inpainting network and propose a classification assistance based inpainting model for BR code (CAII). Experiment results show that our method can efficiently enhance the readability of damaged BR code images.

关键词： Information science Codes Image recognition pattern recognition Error correction codes Decoding Classification algorithms

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An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume 23rd

An Enhanced Coarse-to-Fine Framework for the Segmentation of...

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Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Qian, Dahong Liu, Weiping Li, Hui Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Algorithm and Research Shanghai Aitrox Technology Co. Ltd. Shanghai China National Key Laboratory of Science and Technology on Nano/Micro Fabrication Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Institute of Micro-Nano Science and Technology Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783030718268

In radiation therapy, obtaining accurate boundary of the clinical target volume (CTV) is the vital step to decrease the risk of treatment failures. However, it is a time-consuming and laborious task to obtain the delineation by hand. therefore, an automatic algorithm is urgently needed to realize accurate segmentation. In this paper, we propose an enhanced coarse-to-fine frameworkto automatically fuse the information of CT, T1 and T2 images to get the target region. this framework includes a coarse-segmentation stage to identify the region of interest (ROI) of targets and a fine-segmentation stage to iteratively refine the segmentation. In the coarse-segmentation stage, the F-loss is proposed to keep the high recall rate of the ROI. In the fine segmentation, the ROI of target will be first cropped according to the ROI obtained by coarse-segmentation and be fed into a 3D-Unet to get the initial results. then, the prediction and medium features will be set as additional information for the next one network to refine the results. When evaluated on the validation dataset of challenge of Anatomical Brain Barriers to Cancer Spread (ABCs), our method won the 3th place in the public leaderboard. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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Human Activity recognition: Fight Detection using Long term Recurrent Convolutional Network (LRCN)

Human Activity Recognition: Fight Detection using Long term ...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Ngrddy Jhnvi S.K. Bhrdwj Ajay Sharma Shamneesh Sharma Ankur Sodhi Saikat Gochhait School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Delivery and Student Success upGrad Education Private Limited Bangalore Karnataka India Enterprise Businees and Career Services upGrad Education Private Limited Bangalore Karnataka India Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed University Pune India

ISBN: (数字)9798350368710

ISBN: (纸本)9798350368727

Human Activity recognition for Fight Detection is an important research domain aimed at automatically identifying patterns indicative of physical altercations. Leveraging deep learning models like CNNs and RNNs, this approach extracts features from video frames to recognize fight-related behavioural patterns. the primary objective is to develop a machine learning model capable of autonomously detecting instances of fights in video footage, using techniques such as the Long-term Recurrent Convolutional Network (LRCN). Training involves a comprehensive dataset encompassing examples of both fights and non-fight activities, with model performance evaluated using standard metrics. Each frame undergoes individual analysis by the model to predict the presence of any indications of a fight. the model predicts the action with an accuracy of 98.03% and the movements given as input can be categorized as fights or no fights.

关键词： Training Measurement Industries Predictive models Feature extraction pattern recognition Convolutional neural networks Object recognition Human activity recognition Standards

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