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Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

computers, Materials & Continua 2022年第4期71卷 255-268页

作者： R.Krishnaswamy B.Sivakumar B.Viswanathan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India School of Computing SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Factually of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

关键词： Biomedical signals ecg disease diagnosis artificial intelligence parameter tuning gru model

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Augmented Reality in Higher Education: Literature Review 25

Augmented Reality in Higher Education: Literature Review

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Rahayu, Anita Zamahsari, Gamal Kusuma Adi, Puput Dani Prasetyo Arifuddin, Rahman Dirgantara, Wahyu Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Digital Language Learning Center Communication Science Department Jakarta11480 Indonesia Research Center for Telecommunication Bandung Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350378573

Augmented Reality (AR) is rapidly transforming educational landscapes, offering an immersive and interactive learning experience. This literature mapping review explores the current landscape of AR in higher education research. By systematically analyzing peer-reviewed publications in the last five years from 2019 to 2023, the review identifies key trends, application areas, author collaboration patterns, and research foci, were extracted, analyzed, and visualized using R and VOSViewer. The analysis reveals a growing body of research, with a surge in publications after 2020. science, Technology, engineering, and Mathematics (STEM) subjects dominate AR applications, highlighting its potential to visualize abstract concepts. The review further investigates the impact of AR on student outcomes, including engagement and learning processes. It also examines the geographical distribution of research and identifies knowledge gaps for future exploration. Overall, this review provides a comprehensive map of AR in higher education research, laying the groundwork for further investigation into its pedagogical effectiveness and broader implementation across disciplines. © 2024 IEEE.

关键词： Adversarial machine learning

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Identification and Visualization of Spatial and Temporal Trends in Textile Industry

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computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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Machine Learning Classification Model for Identifying Internet Addiction among University Students 2

Machine Learning Classification Model for Identifying Intern...

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2nd International Conference on computer Technologies, ICCTech 2023

作者： Theng, Tan Hui Kit, Mun Hou Handayani, Dini Department of Mechatronic and Biomedical Engineering Faculty of Engineering and Science Universiti Tunku Abdul Rahman Bandar Sungai Long Selangor Malaysia Computer Science Department Kulliyyah of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9781665455824

In this era of globalization, Internet addiction is a concerning issue, especially among university students as they are required to use the internet for academic purposes. However, things might go wrong when they are addicted to the Internet as the Internet does not only provide knowledge but also entertainment such as music, videos, games, social media, etc. Internet addiction was exposed to the public when Young introduced Internet addiction in her study as well as an assessment for Internet addiction known as Young’s Internet addiction test (IAT) which is a questionnaire. Nonetheless, there are some issues associated with the questionnaire regarding the integrity and literacy of the participants as well as the experience of the specialist which might introduce inconsistencies in the assessment of one’s Internet addiction level. Hence, the machine learning algorithm is introduced to replace the conventional assessment method for Internet addiction. In this study, three machine learning models are developed and compared. The three models include convolutional neural network (CNN), K-nearest neighbours (KNN), and logistic regression (LR). The low Alpha power band of the EEG data is transformed into spectrograms and utilized as the input for the machine learning models. The spectrograms are presented as images and fed into the CNN model. On the other hand, as KNN and LR could not take in images as the input data, the magnitude of each frequency in every time segment of each spectrogram is computed and fed into the KNN and LR. The results show that CNN gives the best performance in terms of overall accuracy, precision, recall, and F1-score, while KNN gives the most consistent performance. © 2023 IEEE.

关键词： Convolutional neural networks

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Frequency Domain Adaptive Learning Algorithm for Thoracic Electrical Bioimpedance Enhancement

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computers, Materials & Continua 2022年第9期72卷 5713-5726页

作者： Md Zia Ur Rahman S.Rooban P.Rohini M.V.S.Ramprasad Pradeep Vinaik Kodavanti Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationK L UniversityVaddeswaramGuntur522502Andhra PradeshIndia Department of Computer Science and Engineering Faculty of Science and TechnologyICFAI Foundation for Higher EducationHyderabad500082TelanganaIndia Department of Electrical Electronics and Communication EngineeringGITAM Institute of TechnologyGITAM Deemed to be UniversityVisakhapatnam530045Andhra PradeshIndia

The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac *** measuring cardiac signal it is contaminated with *** commonly encountered artifacts are Baseline wander(BW)and Muscle artifact(MA),these are physiological and *** the nature of these artifacts is random,adaptive filtering is needed than conventional fixed coefficient filtering *** address this,a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical *** proposed block least mean square(BLMS)algorithm is mathematically normalized with reference to data and *** normalization leads,block normalized LMS(BNLMS)and block error normalized LMS(BENLMS)*** adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological *** ability of these techniques is measured by calculating signal to noise ratio improvement(SNRI),Excess Mean Square Error(EMSE),and Misadjustment(Mad).Among the considered algorithms,the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter ***,this adaptive artifact canceller is suitable for real time applications like wearable,remove health care monitoring units.

关键词： Adaptive learning artifact canceller block processing frequency domain thoracic electrical bioimpedance

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Fingerprint Liveness Detection with Voting Ensemble Classifier 6

Fingerprint Liveness Detection with Voting Ensemble Classifi...

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6th International Conference on Information Technology, InCIT 2022

作者： Sittirit, Napahatai Mongkolwat, Pattanasak Thaipisutikul, Tipajin Supratak, Akara Chen, Zhi-Sheng Wang, Jia-Ching Mahidol University Faculty of Information and Communication Technology Thailand Providence University Department of Computer Science and Information Engineering Taiwan National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665489126

Detecting a user's fingerprint is a common verification process in many daily products such as smartphones and laptops. The convenience makes it popular, but this method is vulnerable to a presentation attack. Any fingerprint can be copied onto materials such as wood glue and gelatin, using only a few simple steps. Therefore, detecting whether the fingerprint comes from a live person is essential. In this paper, we proposed a method that employs a voting ensemble classification model to aggregate predictions from multiple individually trained machine learning models to determine whether an input fingerprint image is a live or a fake one. The input image is first pre-processed with a wavelet denoising algorithm, then Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) are used for feature extraction. Next, we train a Voting Ensemble Classifying Model, utilizing predictions from several trained models, to find the majority vote for fingerprint liveness detection. According to the performance on the public LivDet 2015 dataset, the proposed method achieved better accuracy classification error (ACE) compared to the state-of-the-art models on three out of four sensor types: Greenbit (ACE=0.95%), Digital Persona (ACE=3.71%), and Hi Scan (ACE=1.39%). © 2022 IEEE.

关键词： Local binary pattern

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Enhancing IoT Data Security: Integrating Elliptic Galois Cryptography with Matrix XOR Steganography and Adaptive Firefly Optimization 8

Enhancing IoT Data Security: Integrating Elliptic Galois Cry...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Anuradha, K. Alam, Irfan Nair, Deveendra Menon Narayanan Kiran, Ajmeera Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Telangana India Scai Vit Bhopal University Cyber Security and Digital Forensics Division Kothari Kalan Sehore India Mlr Institute of Technology Department of Computer Science and Engineering Hdyerabad India

ISBN: (纸本)9798350376425

Securing the uninterrupted data flow is crucial in the fast-advancing Internet of Things (IoT) market. This work investigates the incorporation of elliptic Galois cryptography and matrix XOR coding steganography to improve data security. The suggested solution employs the elliptic Galois cryptography protocol to encrypt sensitive data from diverse medical records. The private information is embedded within images through the matrix XOR encoding steganography algorithm. The Adaptive Firefly optimization approach is used to improve the efficiency of utilizing cover blocks for steganography. The effectiveness of the proposed technique is evaluated using various modern approaches. We analyze the efficiency and security of the proposed approach by comparing it with a previous hybrid method employing the New York State Department of Health dataset. The results unequivocally validate that our approach offers enhanced safety in Internet of Things (IoT) applications. © 2024 IEEE.

关键词： Steganography

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On Supervised Class-Imbalanced Learning: An Updated Perspective and Some Key Challenges

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第6期3卷 973-993页

作者： Das, Swagatam Mullick, Sankha Subhra Zelinka, Ivan Indian Statistical Institute Electronics and Communication Sciences Unit Kolkata700108 India VŠB-TUO Department of Computer Science Faculty of Electrical Engineering and Computer Science Czech Republic IT4Innovations National Supercomputing Centre Ostrava70800 Czech Republic

The problem of class imbalance has always been considered as a significant challenge to traditional machine learning and the emerging deep learning research communities. A classification problem can be considered as class imbalanced if the training set does not contain an equal number of labeled examples from all the classes. A classifier trained on such an imbalanced training set is likely to favor those classes containing a larger number of training examples than the others. Unfortunately, the classes that contain a small number of labelled instances usually correspond to rare and significant events. Thus, poor classification accuracy on these classes may lead to severe consequences. In this article, we aim to provide a comprehensive summary of the rich pool of research works attempting to combat the adversarial effects of class imbalance efficiently. Specifically, following a formal definition of the problem of class imbalance, we explore the plethora of traditional machine learning approaches aiming to mitigate its adversarial effects. We further discuss the state-of-the-art deep-learning-based approaches for improving a classifier's resilience against class imbalance and highlight the need for techniques tailored for such a paradigm. Moreover, we look at the emerging applications where class imbalance can be a major concern. Finally, we outline a few open problems along with the various challenges emerging with the advent of modern applications, deep learning paradigm, and new sources of data. © 2020 IEEE.

关键词： Object detection

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare

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Procedia computer science 2024年 246卷 2254-2263页

作者： Jarosław Drapała Jerzy Świątek Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wrocław University of Science and Technology Wrocław 50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types—some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented.

关键词： Multidimensional scaling Kernel density estimation Random forest regression Support Vector Machine

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Quality of service aware routing protocols in wireless multimedia sensor networks: survey

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International Journal of Information Technology (Singapore) 2022年第2期14卷 789-800页

作者： Chiwariro, Ronald .N, Thangadurai Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bangalore India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Centre for Emerging Technologies JAIN (Deemed-to-be University) Karnataka Bangalore India

Improvements in nanotechnology have introduced contemporary sensory devices that are capable of gathering multimedia data in form of images, audio and video. Wireless multimedia sensor networks handles such type of heterogeneous traffic. Since these networks are an emergent of wireless sensor networks, they inherit constraints that exist in these traditional networks. This paper is a survey of characteristics and requirements of wireless multimedia sensor networks and approaches to mitigating existing challenges in these networks. Further presents a review of recent research on multipath routing protocols and multi-channel media access protocols that offer quality of service assurances in handling multimedia data. The survey shows that there still exist bottlenecks in current routing solutions when applied to wireless multimedia sensor networks due to the nature of traffic involved as well as inherent characteristics, requirements and constraints. The findings are useful in the discovery of solutions to the challenges and issues. The authors further suggest approaches and plausible solutions to the problems in order to assist in the development of protocols that achieve quality of service in wireless multimedia sensor networks. © 2020, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Energy efficiency Multi-channel media access control Multipath routing Quality of service

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