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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

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1st international conference on Computational Intelligence for Engineering and Management Applications, CIEMA 2022

1st International Conference on Computational Intelligence f...

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1st international conference on Computational Intelligence for Engineering and Management Applications, CIEMA 2022

ISBN: (纸本)9789811984921

The proceedings contain 68 papers. The special focus in this conference is on Computational Intelligence for Engineering and Management Applications. The topics include: Strokes-Related Disease Prediction Using Machine Learning Classifiers and Deep Belief Network Model;analysis and Detection of Fraudulence Using Machine Learning Practices in Healthcare Using Digital Twin;prediction and Analysis of Polycystic Ovary Syndrome Using Machine Learning;feature Selection for Medical Diagnosis Using Machine Learning: A Review;convolutional Neural Network Architectures Comparison for X-Ray Image Classification for Disease Identification;Secure Shift-Invariant ED Mask-Based Encrypted Medical Image Watermarking;fusion-Based Feature Extraction Technique Using Representation Learning for Content-Based Image Classification;a Comparative Study on Challenges and Solutions on Hand Gesture recognition;design a Computer-Aided Diagnosis System to Find Out Tumor Portion in Mammogram Image with Classification Technique;a Novel soft-computing Technique in Hydroxyapatite Coating Selection for Orthopedic Prosthesis;performance Analysis of Panoramic Dental X-Ray Images Using Discrete Wavelet Transform and Unbiased Risk Estimation;efficient Image Retrieval Technique with Local Edge Binary pattern Using Combined Color and Texture Features;texture and Deep Feature Extraction in Brain Tumor Segmentation Using Hybrid Ensemble Classifier;A Systematic Review on Sentiment Analysis for the Depression Detection During COVID-19 Pandemic;vehicular Adhoc Networks: A Review;the Data Vortex Switch Architectures—A Review;survey on Genomic Prediction in Biomedical Using Artificial Intelligence;a Brief Review on Right to Recall Voting System Based on Performance Using Machine Learning and Blockchain Technology;sentiment Analysis Techniques: A Review;network Traffic Classification Techniques: A Review;remote Production Monitoring System.

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MoiréVib: Micron-level Vibration Detection based on Moiré pattern 24

MoiréVib: Micron-level Vibration Detection based on Moiré ...

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30th international conference on Mobile computing and Networking, ACM MobiCom 2024

作者： Ning, Jingyi Yan, Zhihao Wu, Zhaowei Xie, Lei Wang, Chuyu Chen, Yingying Ye, Baoliu Lu, Sanglu State Key Laboratory for Novel Software Technology Nanjing University China Wireless Information Network Laboratory Rutgers University United States

ISBN: (纸本)9798400704895

Detection and assessment of micro vibrations are crucial tasks in both industrial settings and daily life. However, vibration sensors attached to the target vibrator may introduce potential resonance, and wireless detection methods suffer from severe multipath interference. Fortunately, moiré-based sensing methods have gained recognition in recent years due to their ability to perceive micro motion changes. In this paper, we propose MoiréVib, a micro-vibration detection solution based on moiré patterns for dynamic and high-frequency environments. We attach a printed marker with periodic gratings to the surface of vibration devices to generate moiré patterns, which can amplify micro vibrations due to their low-frequency magnification effect. However, moiré pattern's changes caused by micro vibrations are often overwhelmed by random pixel-level noises, and the limited frame rate of the camera fails to capture high-frequency moiré features. To deal with these problems, we propose a spectrum-based method to refine and enhance the dynamic and micro moiré features. Additionally, we propose a dual-frame-rate-based fusion mechanism to realize high-frequency reconstruction of moiré features. Extensive experimental results show that MoiréVib can realize a median amplitude detection error of 4.37 μm and achieve frequency detection up to 300Hz with a frame rate range of 10∼30 fps. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Vibration analysis

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An Advanced Ensemble Directionality pattern (EDP) based Block Ensemble Neural Network (BENN) Classification Model for Face recognition System

An Advanced Ensemble Directionality Pattern (EDP) based Bloc...

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2022 IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Harshitha, C.J. Bharathi, R.K. Jss Science and Technology University Dept. of Computer Applications Mysuru India

ISBN: (纸本)9781665483162

Developing the face detection and recognition system is one of the most demanding and challenging tasks in the recent days. For this purpose, there are different types of image processing techniques have been developed in the conventional works, which are mainly focusing on detecting the facial images under occlusions. Still, it limits with the major problems of high complexity in algorithm design, does not have the ability to handle large dimensional data, requires more time consumption for training the model, and increased misclassification results. Thus, this paper intends to develop an efficient face recognition system for detecting the occluded faces by implementing an advanced image processing techniques using COFW-100 dataset which includes 507 occluded images. Here, the Gaussian filter technique is utilized to improve the overall quality of original image by suppressing the noise/artifacts. After that, an Ensemble Directionality pattern (EDP) extraction technique is applied to extract the texture features from the normalized image by estimating the similarity between the pixel intensity in different angles of projection plane. It is mainly used to obtain the clear features of the person in the cell of each image frame and, this type of feature extraction helps to increase the accuracy of overall classification system. Then, the Block Ensemble Neural Network (BENN) classification model is deployed to accurately detect the occluded faces by processing the image with separate patterns of features. During experimentation, the performance of both existing and proposed techniques is validated and compared by using various evaluation metrics. Then, the results show that the proposed technique outperforms the other approaches by accurately detecting the face based on the image patterns with separate blocks. © 2022 IEEE.

关键词： Face recognition

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NABP: Social Network Alignment Algorithm based on Pre-trained Language Models 3

NABP: Social Network Alignment Algorithm based on Pre-traine...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Ding, Linjun Chen, Jing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computing Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

The existing social network matching algorithms have problems in processing text attribute information, as they cannot handle polysemy issues of word meanings well and cannot effectively extract deep semantic information from the text. To address the aforementioned issues, this paper introduces pretrained models to extract information from attribute text and proposes a social network alignment algorithm called NABP (Network Alignment based on Pre-trained Language Models). NABP first preprocesses the attribute text data to extract the text feature vectors;then, it uses the DeepWalk algorithm to perform graph representation learning on the two networks to extract their structural features. Next, the attribute feature vectors and structural feature vectors are concatenated to form the final feature vector. Then, a projection function is learned using supervised information to further improve the algorithm's performance. To verify the effectiveness of NABP algorithm, this study conducted experiments on two real social network datasets. The results show that the NABP algorithm performs better than existing algorithms in both the Accuracy and Precision@k metrics, demonstrating the effectiveness of the algorithm and pre-trained language models. © 2023 SPIE.

关键词： Semantics

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Automatic Detecftion of White Blood Cancer From Blood Cells Using Novel Machine Lerning Techniques 8

Automatic Detecftion of White Blood Cancer From Blood Cells ...

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8th international conference on Advanced computing and Communication Systems, ICACCS 2022

作者： Aldrin Karunharan., K. Kanmani Pappa, C. Ruby, E.D Kanmani Sathyasri, B. Edwin Shanthakumar, W. Electronic Engineering International Maritime College Oman Sohar Oman Vel Tech Rangarajan Dr. Sangunthala R&d Institute of Science and Technology Chennai India Vel Tech Rangarajan Dr. Sangunthala R&d Institute of Science and Technology Electronics and Communication Engineering Chennai India Shri Shakthi Institute of Technology and Science Coimbatore India

ISBN: (纸本)9781665408165

The precise recognition of cells is required for the diagnosis of numerous blood illnesses, such as Leukemia and Myeloma, using WBCs or Leukocytes. In order to assist oncologists an automated system is necessary to minimize time and improve accuracy. The segmentation and classification of immature white blood cells are the most crucial phases in such systems. In this aper we have discussed the methodology for detection of white blood cancer caused because of immature leukocytes by using preprocessing, segmentation by Otsu algorithm, Salp swarm algorithm and random forest algorithm. In this paper we have explored the performance of three techniques such as random forest algorithm (RSA) Salp swarm algorithm (SSA) and Statistically enhanced Salp swarm algorithm (SESSA). We have achieved better segmentation and detection accuracy compared to other peer soft computing algorithms. The results shall be beneficiary for hematologists and oncologist as diagnostic reference before proceeding t the treatment strategy © 2022 IEEE.

关键词： soft computing

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Conversational AI Chatbot Using Log pattern Detection

Conversational AI Chatbot Using Log Pattern Detection

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Advances in computing Research on Science Engineering and Technology (ACROSET), international conference on

作者： Tamil Iniyan KS Rakesh M Umar Ahmed Khan Shashikala HK Adeebulla Khan Rahul Das Department of Computer Science and Engineering Jain (Deemed-to-be) University Bangalore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This application is a self-learning and self-healing capability solution for users. The backend system is integrated with multiple data sources relevant to the application, user transaction database, system infrastructure and application services etc., to capture the user interaction with the application and the issues he/she faced before hand and send personalized message to the user. This will provide either a self-healing capability or the agent is ready with the required data to help the customer answer his queries. The solution can be integrated for any domain mobile or web applications application. The application uses log pattern detection to detect a potential outage and solve the upcoming error using different approaches. The application also sends personalized message to the user according to his interests. This application uses different AWS services for creating conversational interfaces between the user and the agent, to provision and manage servers, to store and retrieve data anytime from the web, to train the AI to analyze the log patterns and detect any irregularities etc.

关键词： soft sensors Customer satisfaction Chatbots User experience Transaction databases pattern recognition Servers

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Privacy-Preserving Multi-soft Biometrics through Generative Adversarial Networks with Chaotic Encryption

Privacy-Preserving Multi-Soft Biometrics through Generative ...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Hongying Zheng Hongdie Li Di Xiao Maolan Zhang School of Computer Science Chongqing University Chongqing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The rapid advances in deep learning have enabled the extraction of soft-biometric attributes from faces, raising privacy concerns regarding images collected for face recognition. To address this issue, we propose a privacy-preserving scheme for multiple soft-biometric attributes using Generative Adversarial Networks (GANs), aiming to effectively protect individual privacy while maintaining face recognition system performance. Our scheme incorporates two main modules: 1) The attribute encryption system, which identifies attributes to be hidden through chaotic cryptography and supports the recovery of original attributes. 2) Encoder features are adaptively transformed using Selective Transmission Units (STUs) within a generator featuring an encoder-decoder structure to generate perturbed images. The generated perturbed images are visually similar to the original images but differ in soft-biometric attributes. Experimental results show that our method maintains high face recognition accuracy while preserving privacy, and the quality of the generated images surpasses existing techniques.

关键词： Privacy Accuracy Chaotic communication Face recognition System performance Speech recognition Generative adversarial networks Generators Encryption Protection

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ADHDNet: A DNN Based Framework for Efficient ADHD Detection from fMRI Dataset 9th

ADHDNet: A DNN Based Framework for Efficient ADHD Detection ...

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9th international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Chowdhury, Anjan Chatterjee, Rajdeep Aich, Geetanjali Ghosh, Kuntal ISI Kolkata Ctr Soft Comp Res Kolkata 700108 India KIIT Deemed Be Univ Sch Comp Engn Bhubaneswar 751024 India Heritage Inst Technol Dept Elect & Commun Engn Kolkata 700107 India

ISBN: (纸本)9783031126994;9783031127007

The study of functional connectivity is an evolving field of research in brain network-based analysis of neurological disorders. The interconnection between various brain regions is affected due to different neuro-disorders. Attention Deficit Hyperactivity Disorder (ADHD) has been studied using complex network-based features from the ADHD-200 competition fMRI dataset. The objective is to classify a typically developing subject from one showing a mature stage of acute symptoms (e.g., insufficient attention and/or hyperactivity). ADHD symptoms, being difficult to diagnose efficiently, will, if successfully detected computationally, lead to suitable clinical intervention and improved outcomes. The paper's novelty is to capture the change of functional connectivity between brain regions of interest (ROIs) due to the ADHD syndrome using the brain atlas (MSDL, BASC-64/444) and complex network measures. A novel 5-layered Deep Neural Network (ADHDNet) has been implemented in this paper for efficient computer-aided diagnosis of ADHD. The output is compared with the traditional and best-performing machine learning technique Gradient Boosting as the dataset used is imbalanced between control population and mature-ADHD patients. SMOTE, Random Under (RUS), and Over (ROS) Samplers have been employed to deal with the data imbalance. This study is unique in its focus on the efficient detection of ADHD cases using complex network concepts as the feature extractor. The best performing results are 100% and 93% test-accuracies from BASC-64 + RUS and MSDL + ROS, respectively. The proposed ADHDNet provides consistently excellent and stable results based on evaluation metrics such as F1-score, accuracy, and Area under the ROC Curve(AUC).

关键词： ADHD Classification Connectome Complex network fMRI Deep Neural Network Atlas

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Proceedings - 3rd international conference on Smart Technologies, Communication and Robotics 2023, STCR 2023

Proceedings - 3rd International Conference on Smart Technolo...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

ISBN: (纸本)9798350370867

The proceedings contain 82 papers. The topics discussed include: advanced malware analysis and prevention;design and analysis of Vivaldi antenna by an iterative method;EGFD-AROMA-based pattern forecasting with crime rate level identification;early detection and prognosis of brain tumor from micro array gene data using machine learning classifiers;handwritten digit recognition using fine-tuned convolutional neural network model;design of high gain 5G millimeter wave micro strip patch antenna for wireless applications;using machine learning regression model to predict the optimum election algorithm for parallel and distributed computing systems;a design thinking approach of metaheuristic empowerment for energy-efficient and optimized routing protocol in IoT-enabled wireless sensor networks;and an overview on IoT architecture application layer and security threats.

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3rd international conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

3rd International Conference on Emergent Converging Technolo...

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3rd international conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

ISBN: (纸本)9789819986453

The proceedings contain 56 papers. The special focus in this conference is on Emergent Converging Technologies and Biomedical Systems. The topics include: Smart Health Monitoring System for Elderly People;impact of Covid-19 and Subsequent Usage of IoT;design of Battery Monitoring System for Converted Electric Cycles;image Denoising Framework Employing Auto Encoders for Image Reconstruction;server Access pattern Analysis Based on Weblogs Classification Methods;multilingual Emotion recognition from Continuous Speech Using Transfer Learning;Violence Detection Using DenseNet and LSTM;financial Technology and Competitive Landscape in the Banking Industry of Bangladesh: An Exploratory Focus;on Parameterized Picture Fuzzy Discriminant Information Measure in Medical Diagnosis Problem;review on Deep Learning-Based Classification Techniques for Cocoa Quality Testing;a Curated Study on Machine Learning Based Algorithms and Sensors for Drone Technology in Various Application;automatic Detection of Coagulation of Blood in Brain Using Deep Learning Approach;deepPose: A 2D Image Based Automated Framework for Human Pose Detection and a Trainer App Using Deep Learning;Phylogenetic Study of Surface Glycoprotein (S1 Spike Protein) Sequence of SARS-CoV-2 Virus;pervasive and Wearable computing and Networks;power of Image-Based Digit recognition with Machine Learning;open-Source Gesture-Powered Augmented Reality-Based Remote Assistance Tool for Industrial Application: Challenges and Improvisation;enhancing Biometric Performance Through Mitigation of Sleep-Related Breaches;Neural Network Based CAD System for the Classification of Textures in Liver Ultrasound Images;fuzzy Vendor–Buyer Trade Credit Inventory Model-Pentagonal Numbers in Permissible Limits Delay in Account Settlement with Supervised Learning;a Comparative Survey on Histogram Equalization Techniques for Image Contrast Enhancement.

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