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An Experiment Towards the Detectability of Image Steganography Without Using Classifier

An Experiment Towards the Detectability of Image Steganograp...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Ayidh Alharbi Faculty of Computers and Information Technology University of Tabuk Tabuk KSA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Image steganography become one of the important fields of cybersecurity. It studies the potential of multimedia security in order to protect confidential information In general. In specific, it belongs to information hiding. However, Image steganography process results in stego images. In this study, two main experiments of image steganography detectability without using classifier with help of Image Processing, Image Quality, Feature Extraction, and Significant Statistical Measure. Several features involves one or many parameters that help to study experimental images. For example: Image histogram, Detection line, Structural Similarity Index Measures (SSIM), And Sum of Arrays Index (Sums). Although, It study the functionality of Image features to highlight the process of its detectability.

关键词： Steganography Histograms Visualization Image recognition Feature extraction Robustness pattern recognition Indexes Information technology Surface treatment

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Towards Generalized ML Model in Automated Physiological Arousal computing: A Transfer Learning-Based Domain Generalization Approach

Towards Generalized ML Model in Automated Physiological Arou...

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2022 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Fang, Ruijie Zhang, Ruoyu Hosseini, Elahe Parenteau, Anna M. Hang, Sally Rafatirad, Setareh Hostinar, Camelia E. Orooji, Mahdi Homayoun, Houman University of California ECE Department DavisCA United States University of California Department of Psychology DavisCA United States

ISBN: (纸本)9781665468190

Physiological signal-based pattern recognition has progressed significantly, such as automated pain assessment and stress detection. Public datasets provide a research platform to conduct machine learning studies. However, models trained from public datasets easily overfit that specific dataset and do not apply to unseen data collected in real-life scenarios. This paper proposes to use the transfer learning-based domain generalization technique to generalize the models to solve this issue. Data from different training domains are generalized, i.e., the dissimilarity is minimized by the proposed approach such that the model trained is generalized. We proved that the generalized model is more adaptive to new unseen data. Experiments have been done on the BioVid heat pain dataset and WESAD stress dataset, and results showed that our proposed methods significantly improve the model performance on new unseen data. © 2022 IEEE.

关键词： pattern recognition

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Proceedings of 2022 2nd international conference on computing Advancements: Age of computing and Augmented Life, ICCA 2022

Proceedings of 2022 2nd International Conference on Computin...

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2nd international conference on computing Advancements: Age of computing and Augmented Life, ICCA 2022

ISBN: (纸本)9781450397346

The proceedings contain 77 papers. The topics discussed include: enhanced characterization performance of propionylation PTM utilizing multiple feature fusion;ramifications of corruption perception index: an exploratory data analyses using DBSCAN;isolated Bangla handwritten character classification using transfer learning;Hoyt wireless fading channel capacity analysis using large limit argument approximation;COV-HM: prediction of COVID-19 patient’s hospitalization period for hospital management using SMOTE and machine learning techniques;performance analysis of parallel overlapping community detection algorithms in large-scale social networks;solving numerical puzzles and sudoku using pattern recognition and image processing;analyzing the effect of COVID-19 on mental health based on Bangladeshi university students;spectrum impact analysis of fault proneness statement for improved fault localization;phishing website detection using deep learning;and mapping multimodel scrolling techniques for foot-based interaction depending on the cursor position.

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soft Real-time Emotion-aware System through Human Physiological Signals using Pervasive Devices

Soft Real-time Emotion-aware System through Human Physiologi...

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IEEE international conference on Communication, Networks and Satellite (ComNetSat)

作者： Feri Setiawan Sunder Ali Khowaja Computer Science School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia School of Computing Faculty of Computing Digital and Data Technological University Dublin Dublin Ireland

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

The concept of connected things that involves emotional aspects has been raised as a new research issue known as “emotional IoT”. One of the challenges is to recognize the emotion in real-time without interfering the user’s task or activity. However, most of the existing works on emotion recognition deal with high sampling rate devices and thus need complex arrangement. In this study, we present a framework to recognize four emotions based on human physiological signals using pervasive devices with low sampling rate. The employment of low sampling rate devices may degrade the classification accuracy due to the data offsets or calibration errors. Our study proposes a set of energy deviation features to overcome the accuracy degradation caused by low sampling rate devices. The recursive feature elimination method is applied to avoid overfitting by selecting the most important features and reduce the computational overhead in real-world implementation. Experimental results show that the proposed approach achieves 92.01% and 91.75% accuracy in terms of valence and arousal, respectively. The realization of the proposed approach is done in an end-to-end soft real-time emotion recognition system using smartphone and smartwatch devices. The performance of our system was evaluated under laboratory setup, resulting in 46.3 milliseconds of response time on the average for recognizing emotions.

关键词： Performance evaluation Emotion recognition Wearable Health Monitoring Systems Accuracy Satellites Feature extraction Real-time systems Physiology Time factors Overfitting

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Privacy-Preserving Average Consensus for Multi-Agent System with Event-Triggered Method

Privacy-Preserving Average Consensus for Multi-Agent System ...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Shuyu Shi Xinyang Cao Qingmiao Zhang International Department Chengdu Foreign Languages School Chengdu China

The paper considers the privacy-preserving and communication constraints for multi-agent systems. Firstly, a lightweight, decentralized, time-varying transformation method is introduced to prevent information leakage during information exchange between agents. This approach requires less computation and is compatible with systems with limited computing resources. Secondly, the paper proposes an event-triggered mechanism that considers both privacy-preserving and communication constraints to avoid continuous updates of the privacy-preserving controller. By using intermittent communication mode, this approach effectively reduces computing and communication resources compared to traditional time-triggered methods. Overall, these methods offer efficient solutions for privacy and communication challenges in multi-agent systems. The finite-time privacy-preserving control protocol is validated through numerical simulation examples to demonstrate its effectiveness.

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Design and Implementation of a Wireless Monitoring Application for Pregnant Women's Fetal Movement

Design and Implementation of a Wireless Monitoring Applicati...

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Cybersecurity, Internet of Things and soft computing (CITSC), Asia-Europe conference on

作者： Manping Qin Yong Xu Choon Kit Chan Zhiling Chen Caitong Zhu School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Faculty of Engineering and Quantity Surveying INTI International University Nilai Negeri Sembilan Malaysia

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

The fetal movement signal represents the most intuitive indicator of the health of the fetus and the mother. In order to effectively monitor the health status of the fetus and the pregnant woman in the third trimester, an application was designed based on the Android operating system for real-time monitoring of fetal movement in pregnant women. Using Android Studio as the development platform, the SQLite database was utilized to facilitate user registration and login. The low-power Bluetooth API is utilized to establish the connection between the mobile terminal and the wireless fetal movement monitor for pregnant women, enabling the collection of raw fetal movement data. Real-time transmission of data to the smartphone via low-power Bluetooth is achieved, followed by the processing and recognition of fetal movement signals on the app, culminating in the visualization of the recognition results. Through rigorous testing and application, the system facilitates the visualization of fetal health data, addressing family concerns about the health of pregnant women and fetuses, and is applicable in the field of fetal health monitoring.

关键词： Pregnancy Wireless communication Bluetooth Databases Data visualization Fetus Real-time systems Biomedical monitoring Monitoring Smart phones

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12th international Congress in Telematics and computing, WITCOM 2023

12th International Congress in Telematics and Computing, WIT...

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12th international Congress in Telematics and computing, WITCOM 2023

ISBN: (纸本)9783031453151

The proceedings contain 35 papers. The special focus in this conference is on Telematics and computing. The topics include: A Novel Method Based on Gunnar Farneback Method, Mathematical Morphology, and Artificial Vision for Flow Analysis in Electrochemical Reactors;search Space Reduction in Road Networks for the Ambulance Location and Allocation Optimization Problems: A Real Case Study;development of a Web-Based Calculator to Simulate Link Budget for Mobile Communications Systems at Urban Settlements;scientific Information Management System for Multidisciplinary Teams;air Quality Prediction in Smart Cities Using Wireless Sensor Network and Associative Models;User Interface of Digital Platforms Used TDHA Patients: Case Study in Educational Environment;recognition of Pollen-Carrying Bees Using Convolutional Neural Networks and Digital Image Processing Techniques;enhancing Air Quality Monitoring in Mexico City: A Hybrid Sensor-Machine Learning System;multi-labeling of Malware Samples Using Behavior Reports and Fuzzy Hashing;APOS is Not Enough: Towards a More Appropriate Way to Estimate Computational Complexity in CIC Decimation Architectures;Computational Simulation Applied to 3.5 GHz Band Microstrip Yagi Array Antenna Design for 5G Technology Mobile Wireless Device;3D Point Cloud Outliers and Noise Reduction Using Neural Networks;Development and Coding of a Data Framing Protocol for IoT/LPWAN Networks Based on 8-Bit Processing Architectures;performance Analysis of Variable Packet Transmission Policies in Wireless Sensor Networks;about a Sentiment Analysis Technique on Twitter and Its Application to Regional Tourism in Quintana Roo;comparative Study of pattern recognition Techniques in the Classification of Vertebral Column Diseases;security Verification of Instant Messaging Cryptographic Protocols;Elaboration of an Information System to Improve Operational Discipline in PEMEX with LAGS Methodology;ICIS: A Model for Context-Based Classification of Sensitive Personal In

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pattern recognition and Modelling in Electrocardiogram Signals: Early Detection of Myocardial Ischemia and Infraction

Pattern Recognition and Modelling in Electrocardiogram Signa...

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international conference on Edge computing and Applications (ICECAA)

作者： R. Muthalagu Ramachandran R Anuradha. T Anupama PH Jose Anand A Department of Electronics and Communication Engineering Agni College of Technology Tamil Nadu Department of Bio Medical Engineering Rajalakshmi Engineering College Tamil Nadu Department of Electrical and Electronics Engineering KCG College of Technology Tamil Nadu

pattern recognition and modeling in electrocardiogram (ECG) signals play an important role in the early detection of myocardial ischemia and infarction, which are serious cardiovascular diseases that require immediate medical attention. This study provides an overview of techniques used for pattern recognition and modeling in ECG signals to aid in the early detection of myocardial ischemia and infarction. First, the article discusses the importance of ECG signals in the diagnosis and monitoring of heart diseases. It highlights specific ECG changes associated with myocardial ischemia. Understanding these ECG patterns is critical for accurate diagnosis and timely intervention. Next, the study explores various techniques used for pattern recognition and modeling in ECG signals. These techniques include classical signal processing methods, filtering, feature extraction and classification algorithms, and advanced approaches such as machine learning and deep learning. The study discusses the strengths and limitations of each technique and their applications in the diagnosis of myocardial ischemia and myocardial infarction. Also, the study addresses challenges in analyzing ECG signals such as noise, artifact interference, and the need for real-time processing. It also discusses the importance of a comprehensive database of annotated ECG signals for training and testing pattern recognition models. Finally, the potential benefits of early detection of myocardial ischemia and myocardial infarction include improved patient outcomes, reduced morbidity and mortality, and efficient use of healthcare resources. This emphasizes the need for further research and development in pattern recognition and modeling techniques to improve the accuracy and efficiency of early detection methods.

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A Review of Digital Forensics Techniques and Emerging Trends

A Review of Digital Forensics Techniques and Emerging Trends

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Cognitive computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Priya Sharma Mohd Waris Khan Department of Computer Application Integral University Lucknow India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

A scientific approach to investigating digital attacks and crimes is employed in digital forensics, commonly referred to as computer forensics. The field of digital forensics requires a lot of work. Investigators begin by searching electronic devices for evidence and storing the information on a safe drive. The information is then examined and recorded. Next, they hand over the digital evidence to the police, who can use it to solve crimes or use it as evidence in court to establish the guilt of the accused. Analysing every device that can store digital data is now included in the enlarged definition of digital forensics, which was first coined as a synonym for computer forensics. The foundation of behavioral forensics can be regarded as machine learning, which has strong ties to artificial intelligence. For the purpose of stopping any unlawful activity, software-based pattern recognition can handle enormous volumes of data using machine learning. Using networked software and tools for remote analysis is one way to employ machine learning.

关键词： Industries Hands Reviews Law enforcement Digital forensics Machine learning Market research software pattern recognition Faces

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BeAwareOfYourAct: A Framework for Behavioural Action Detection in Workplace through Deep Learning Analysis and Augmented Action pattern recognition 2

BeAwareOfYourAct: A Framework for Behavioural Action Detecti...

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2nd international conference in Information and computing Research, iCORE 2022

作者： Isles, Edward Humphrey C. Balahadia, Francis F. College of Computer Studies Laguna State Polytechnic University Philippines

ISBN: (纸本)9798350333909

The purpose of this study is to propose a system framework that addresses the problem in monitoring malicious behavior of an individual towards someone in the same vicinity through multi-person detection video processing. The real-time video rendering analysis and behavioral action detection is an efficient method in line with pervasive or any event related atrocious behavior towards an individual. In the development of the system several algorithms will be utilized such as object detection, action recognition algorithms and techniques (e.g. Human Activity recognition, Supervised Learning Data Representation, etc.) which will heavily rely on Regions with Convolutional Neural Network (RCNN). The capability of Artificial Intelligence (A.I.) to analyze real-time video rendering through the training data to be supplied as a point of reference in deep learning analysis, any act that might cause discomfort to the victim can be detected to be reported immediately to the Officer-in-charge preventing the maltreatment to further escalate. In order to accomplish the said study that will serve as a pillar for the system development. It is concluded that this system framework of real-time monitoring is a great approach in preventing certain abuse from starting and will make the conduct of investigation more precise and efficient should a harassment come into place. Through this framework it can make the vicinity more conducive to healthy communication and prevent intimidating words to be casually mentioned. © 2022 IEEE.

关键词： Object detection

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