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pattern Classification Can Detect Prodromal Alzheimer’s Neurodegenerations 27th

Pattern Classification Can Detect Prodromal Alzheimer’s Neu...

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27th international conference on pattern recognition, ICPR 2024

作者： Przybyszewski, Andrzej W. Nowacki, Jerzy P. Drabik, Aldona Polish-Japanese Academy of Information Technology Warszawa02-008 Poland Department of Neurology UMass Medical School WorcesterMA01655 United States

ISBN: (纸本)9783031876592

Alzheimer’s disease (AD)-related neurodegeneration can begin several decades before the first symptoms appear, and AD biomarkers have only recently been identified. Our research focused on identifying AD-related biomarkers in healthy subjects. We analyzed Biocard data using a group of 150 subjects in different AD stages, with 40 AD patients compared to normal subjects. Using the granular computing method, we identified sets of attributes related to various stages of the disease. We applied this classification to the psychophysical test results of normal subjects to determine if some cases might show granular pattern similarities to groups with AD. Earlier detection can uncover patterns invisible to neuropsychologists in the cognitive like executive or immediate/delayed memory functions. these patterns may help to determine if a patient is getting amnestic or non-amnestic early symptoms that lead to dementia or not. Our analysis of Biocard data identified granules that classify cognitive attributes with disease stage scores (CDRSUM - Clinical Dementia Rating Scale Sum of Boxes). By applying these rules to 21 normal subjects with a CDRSUM of 0, we predicted that one subject might develop mild dementia (CDRSUM > 4.5) with a mixture of executive and cognitive test results. Another patient might get very mild dementia (CDRSUM > 2.25), but with dominating memory problems which might lead to serious amnestic symptoms. Other patients might develop questionable impairment (CDRSUM > 0.75). Our AI-powered method can identify patterns in cognitive attributes in normal subjects that might indicate their pre-dementia amnestic or non-amnestic stages, which may be invisible to neuropsychologists. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Granular computing

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An Empirical Design of IoT based Health Surveillance Scheme for Coronavirus Affected Patients 8

An Empirical Design of IoT based Health Surveillance Scheme ...

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8th IEEE international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Swarna, M. Geetha, R. Saranya, G. Shakthi Murugan, K.H. Gnanaprakasam, C. Dr. M.G.R. Educational and Research Institute University Department of Electronics and Communication Engineering Tamilnadu Chennai600095 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore641008 India Narayana Engineering College Department of Electronics and Communication Engineering AndhraPradesh Gudur524101 India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai600123 India

ISBN: (纸本)9798350347791

In the year of 2019, a major destruction was raised to all over the Country called as Coronavirus, which is also termed as Covid-19. this is one of the serious threat and many people lost their lives due to the affection of Coronavirus disease. Due to the spread ratio and nature, nobody is interested to properly monitor the patients who are all affected with this disease. So, that a new technology is required to monitor the health conditions of patients and instantly reporting the details to the respective person and guardian as well. As technology advances, doctors are constantly on the lookout for new automated gadgets that might make it simpler to detect abnormalities in the human body. the Internet of things makes it possible to create cutting-edge, transparent healthcare support structures. this paper introduced a new health monitoring scheme called Covid Patients Health Analyzer (CPHA), in which this methodology is associated with the help of trending technology called Internet of things (IoT). this system introduces an Internet of things assisted patient health surveillance technology, which aids medical professionals in remotely measuring the blood concentration, pulse rate, respiration level and temperature of the respective patient. Both the person's pulse rate and blood oxygen sufficiency levels are read using an intelligent sensor called MAX3100, whereas the individual's temperature is scanned with a powerful sensor called DS18B20. Using a humidity identification sensor, one may determine the relative humidity of a given space. All required information will be encoded and decoded by the ESP32 module before being processed. the patients Fingers are hooked up to sensors as well as readings are shown on a respective computer or smartphone. After putting the suggested system through its paces, the working is clearly tested and it delivers the desired results in good manner. By monitoring the process remotely, medical personnel no longer need to worry about c

关键词： Coronavirus

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8th international conference on pattern recognition and Machine Intelligence, PReMI 2019

8th International Conference on Pattern Recognition and Mach...

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8th international conference on pattern recognition and Machine Intelligence, PReMI 2019

ISBN: (纸本)9783030348687

the proceedings contain 131 papers. the special focus in this conference is on pattern recognition and Machine Intelligence. the topics include: Spectral Feature Based Kannada Dialect Classification from Stop Consonants;Incorporation of Neighborhood Concept in Enhancing SOM Based Multi-label Classification;Prediction of Performance Indexes in CNC Milling Using Regression Trees;dyscalculia Detection Using Machine Learning;temporal Dynamics of the Brain Using Variational Bayes Hidden Markov Models: Application in Autism;robust Identification of Dense or Sparse Crowd Based on Classifier Fusion;sustained Self-Supervised Pretraining for Temporal Order Verification;retraining Conditions: How Much to Retrain a Network After Pruning?;m-ary Random Forest;exponentially Weighted Random Forest;automatic Attribute Profiles for Spectral-Spatial Classification of Hyperspectral Images;instance Ranking Using Data Complexity Measures for Training Set Selection;A Multi-class Deep All-CNN for Detection of Diabetic Retinopathy Using Retinal Fundus Images;real-Time Vehicle Detection in Aerial Images Using Skip-Connected Convolution Network with Region Proposal Networks;Dual CNN Models for Unsupervised Monocular Depth Estimation;Deep CNN for Single Image Super Resolution Using Skip Connections;detection of Image Manipulations Using Siamese Convolutional Neural Networks;DaNSe: A Dilated Causal Convolutional Network Based Model for Load Forecasting;Multichannel CNN for Facial Expression recognition;data Driven Sensing for Action recognition Using Deep Convolutional Neural Networks;gradually Growing Residual and Self-attention Based Dense Deep Back Projection Network for Large Scale Super-Resolution of Image;enhanced Depression Detection from Facial Cues Using Univariate Feature Selection Techniques;3D-GCNN - 3D Object Classification Using 3D Grid Convolutional Neural Networks;a New Steganalysis Method Using Densely Connected ConvNets.

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Evaluating Deep Learning Techniques for Real-time Facial Expression recognition from Video Streams

Evaluating Deep Learning Techniques for Real-time Facial Exp...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： M. Jagadeesh B. Baranidharan Department of Computing Technologies SRM Institute of Science & Technologies Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Facial expression recognition has gained significant attention in computer vision due to its wide range of applications in security, personalized services, and evaluating online user engagement. this research explores deep learning techniques for real-time facial expression detection in video sequences. the proposed approach utilizes a framework based on the Viola-Jones face detection algorithm along with feature extraction techniques like Scale-Invariant Feature Transform (SIFT) and Local Directional pattern (LDP). To optimize feature selection, the Deer Hunting Optimization (DHO) technique was employed. these features were then combined with a Heuristically Modified Recurrent Neural Network (HM-RNN) to enhance recognition accuracy. the model was tested using image sequences from the Affectiva-MIT Facial Expression Dataset (AM-FED). Results indicated that the SIFT-LDP-DHO-HM-RNN method consistently achieved superior accuracy compared to other methods. this study demonstrates the potential of advanced deep learning frameworks in improving real-time facial expression recognition systems.

关键词： Deep learning Accuracy Recurrent neural networks Face recognition Video sequences Transforms Streaming media Feature extraction Real-time systems Streams

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A Graph-Based Information Fusion Approach for ADHD Subtype Classification 19

A Graph-Based Information Fusion Approach for ADHD Subtype C...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Huang, Wuliang Jianga, Xinlong Gaoa, Chenlong Zhanga, Teng Xing, Yunbing Chen, Yiqiang Zheng, Yi Li, Jie Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China University of Chinese Academy of Sciences Beijing China Capital Medical University Beijing Anding Hospital Beijing China China Unicom Research Institute Beijing China

ISBN: (纸本)9798350346558

Attention deficit hyperactivity disorder (ADHD) is a common childhood mental disorder that encompasses three subtypes. Classifying each subtype has practical significance. However, the gold standard for subtype diagnosis depends on face-to-face consultation with psychiatrists, which is limited by medical resources. this paper proposes a graph-based multimodal fusion approach to classify each subtype objectively, alleviating the pressure on psychiatrists. the proposed method leverages heterogeneous signals, including motion and speech, which are significant indicators of ADHD. We construct a personal graph where each child is a vertex, and the similarity of their personal information measures edges. Since the associations between subjects modeled by the personal graph provide rich prior knowledge, we regard the problem of subtype classification as predicting the labels of vertices on a graph. A novel graph neural network model is proposed to enable information passing between children, fusing motion and speech features under the guidance of the personal graph. We design a reading scenario and collect a multimodal dataset containing 56 children with ADHD and 50 typically developing children. Results of ADHD subtype classification demonstrate the practical value of the proposed approach. We also perform ablation studies to verify the validity of the proposed method. © 2022 IEEE.

关键词： Classification (of information)

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Discovering All-chain Set with Direction and Graduality Characteristics over Streaming Time Series 19

Discovering All-chain Set with Direction and Graduality Char...

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作者： Wang, Shaopeng Feng, Chunkai Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Department of Software Engineering Hohhot China

ISBN: (纸本)9798350346558

Since its introduction over five years ago, time series chain has become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. Recent work has generalized the definition of time series chain, and introduced a novel definition of time series chain with directionality and graduality characteristics (TSC-DG) which can significantly enhance both robustness and usability of the original time series chain. However, previous studies on TSCDG process fixed-length time series. In this work, we focus on the issue of all-chain set with direction and graduality characteristics (all-TSCS-DG) mining over streaming time series for the first time, where all-TSCS-DG is the core of current TSCDG researches. We propose an improved Naive algorithm (IN) to solve this problem. Compared to the Naive, the IN guarantees the same space costs and results firstly, secondly is the IN takes two additional optimal strategies to further improve the time efficiency. the basic ideas of these two strategies are both incremental computing. the first one can make the IN update the IB structure at each time-tick incrementally, where the IB is an important data structure that is used to obtain the all-TSCS-DG. the second one makes the IN obtain mining results at current time-tick based on the ones at the last time-tick incrementally. Extensive experiments on real dataset demonstrate the efficiency and effectiveness of the IN. © 2022 IEEE.

关键词： Time series

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Access pattern Hiding in Searchable Encryption 8

Access Pattern Hiding in Searchable Encryption

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8th international conference on Future Internet of things and Cloud, FiCloud 2021

作者： Boucenna, Fateh Nouali, Omar Adi, Kamel Kechid, Samir CERIST Research Center Algeria UQO QC Canada USTHB University Algeria

ISBN: (纸本)9781665425742

Cloud computing is a technology that provides users with a large storage space and an enormous computing power. For privacy purpose, the sensitive data should be encrypted before being outsourced to the cloud. To search over the outsourced data, searchable encryption (SE) schemes have been proposed in the literature. An SE scheme should perform searches over encrypted data without causing any sensitive information leakage. To this end, a few security constraints were elaborated to guarantee the security of the SE schemes, namely, the keyword privacy, the trapdoor unlinkability, and the access pattern. the latter is very hard to be respected and most approaches fail to guarantee the access pattern constraint when performing a search. this constraint consists in hiding from the server the search result returned to the user. the non respect of this constraint may cause sensitive information leakage as demonstrated in the literature. To fix this security lack, we propose a method that allows to securely request and receive the needed documents from the server after performing a search. the proposed method that we call the access pattern hiding (APH) technique allows to respect the access pattern constraint. An experimental study is conducted to validate the APH technique. © 2021 IEEE.

关键词： Sensitive data

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8th international conference on pattern recognition and Machine Intelligence, PReMI 2019

8th International Conference on Pattern Recognition and Mach...

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8th international conference on pattern recognition and Machine Intelligence, PReMI 2019

ISBN: (纸本)9783030348717

the proceedings contain 131 papers. the special focus in this conference is on pattern recognition and Machine Intelligence. the topics include: QIBDS Net: A Quantum-Inspired Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation;colonoscopic Image Polyp Classification Using Texture Features;GRaphical Footprint Based Alignment-Free Method (GRAFree) for Classifying the Species in Large-Scale Genomics;density-Based Clustering of Functionally Similar Genes Using Biological Knowledge;impact of the Continuous Evolution of Gene Ontology on Similarity Measures;DEGnet: Identifying Differentially Expressed Genes Using Deep Neural Network from RNA-Seq Datasets;Survival Analysis with the Integration of RNA-Seq and Clinical Data to Identify Breast Cancer Subtype Specific Genes;Deep Learning Based Fully Automated Decision Making for Intravitreal Anti-VEGF therapy;Biomarker Identification for ESCC Using Integrative DEA;critical Gene Selection by a Modified Particle Swarm Optimization Approach;automated Counting of Platelets and White Blood Cells from Blood Smear Images;neuronal Dendritic Fiber Interference Due to Signal Propagation;Two-Class in Silico Categorization of Intermediate Epileptic EEG Data;employing Temporal Properties of Brain Activity for Classifying Autism Using Machine Learning;Time-Frequency Analysis Based Detection of Dysrhythmia in ECG Using Stockwell Transform;Block-Sparsity Based Compressed Sensing for Multichannel ECG Reconstruction;a Dynamical Phase Synchronization Based Approach to Study the Effects of Long-Term Alcoholism on Functional Connectivity Dynamics;RBM Based Joke Recommendation System and Joke Reader Segmentation;TagEmbedSVD: Leveraging Tag Embeddings for Cross-Domain Collaborative Filtering;On Applying Meta-path for Network Embedding in Mining Heterogeneous DBLP Network;an Improved Approach for Sarcasm Detection Avoiding Null Tweets.

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Interactive Semantic Segmentation With Weak Supervision 22

Interactive Semantic Segmentation With Weak Supervision

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Gong, Lei Wang, Da-Han Wu, Yun Ye, Hai-Li Zhu, Chen-Yan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Medical Diagnostic Systems Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781450396110

At present, the most advanced semantic segmentation model training mainly relies on pixel-level annotation, that is, annotating the category of each pixel of an image. Such annotation usually is time-consuming and expensive, especially for special applications that require expert annotation. the weakly-supervised segmentation method using the point-level supervision information has been investigated which however has great problems that the supervision information is quite limited and the performance is far from fully supervised methods. In this paper, we proposes an novelty interactive image segmentation method based on weak supervision, which allows multiple feedbacks of easily obtained weakly supervised information and improves the efficiency of utility of the supervision information. In the downstream task (interactive image segmentation), supervised information at the point level is used for many times, which makes the connection between pixels in the upstream task become closer and improves the segmentation accuracy. First, image-level tags are used to train the classification network. then the pseudo-semantic labels are generated and put into the interactive segmentation network for training, and an almost completely supervised CNN is obtained, which further improves the performance and provides operability for human-computer interaction. the proposed method achieves promising semantic segmentation results that are close to those obtained by strongly supervised segmentation methods on the PASCAL VOC 2012 datasets. © 2022 ACM.

关键词： Semantic Segmentation

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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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