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Deep Learning assisted tool for Atrial Fibrillation detection using RR Intervals 1

Deep Learning assisted tool for Atrial Fibrillation detectio...

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1st International Conference on Connected systems and Intelligence, CSI 2022

作者： Disha, S. B., Deekshitha A., Anwitha U.M., Kavyashree S.K., Shrikanth Rao R.J., Martis Vivekananda College of Engineering & Technology Dept. of Electronics and Communication Engineering D.K. Puttur India Global Academy of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665458153

Atrial Fibrillation (AF) is a life-threatening heart rhythm disorder. AF diagnosis is very essential and important aspect for healthcare professionals. Early detection of AF using Electrocardiogram (ECG) plays an important role in the clinical practice. Manual interpretation of ECG signals to detect AF is time-consuming and needs higher expertise, and it is subject to variability among experts. Detecting AF in a timely and effective manner still remains a difficult challenge. In this paper, we propose a Deep Learning (DL) based AF detection method using Physionet challenge 2017 dataset. VGG16 architecture is used for the classification purpose. With the help of Discrete Wavelet Transform (DWT) the ECG signals are denoised. The RR intervals are computed and are subjected to VGG16 for classification. The class specific accuracies of normal, AF, and other rhythms are calculated. The proposed method achieves overall accuracy of 97.60%. The proposed method can be used as an assisted tool by the physician in their clinical practice. © 2022 IEEE.

关键词： Electrocardiograms

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A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Metal Wall Application

A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Met...

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International Symposium on Antennas and Propagation (ISAP)

作者： Hsin-Lung Su You-Sheng Zhan Dept. of Computer and Communication National Pingtung University Taiwan Dept. of Computer Science and Information Engineering National Pingtung University Taiwan

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

4×4 CPW MIMO Antenna array with an artificial magnetic conductor (AMC) reflector is presented for Wi-Fi7 access point on a metal wall application in the study. The 10-dB impedance of the integrated MIMO antenna array covers the Wi-Fi 7 operating bands, and the transmission coefficients are under -15 dB in the whole Wi-Fi7 bands. This proposed integrated MIMO antenna is low profile and has good total radiation efficiency (over 60%). The height of the integrated MIMO antenna array is 6.325 mm.

关键词： Transmitting antennas Metals Directive antennas Reflector antennas Conductors Coplanar waveguides Impedance Wireless fidelity Antenna arrays Antenna radiation patterns

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via Machine Learning Techniques

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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Emerging Technology (INCET), International Conference for

作者： Amriti Gupta Kunal Nayyar Harshit Chopra Neha Gahlan Divyashikha Sethia Dept. of Software Engineering Delhi Technological University Dept. of Electronics & Communication Engineering Delhi Technological University Dept. of Computer Engineering Delhi Technological University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Emotions, intricate mental states shaped by neu-rophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. The Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. This paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. This research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. The exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested.

关键词： Emotion recognition Accuracy Sleep Cognitive processes Machine learning Data collection Brain modeling

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Identification of Fraudulent Transactions for Enhanced Credit Card Fraud Detection 4

Identification of Fraudulent Transactions for Enhanced Credi...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Gupta, Monica Gayathri, V. Narula, Rachna Kumar, Vijay Kaur, Surinder Singh, Neetu Bharati Vidyapeeth's College of Engineering Dept. of Electronics and Communication Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Information and Technology New Delhi India

ISBN: (纸本)9798350387490

With the flourishment of the digital era and the evolution of new techniques and technology, this paper highlights the ever-growing risk of credit card fraud. The advancing technology has provided fraudsters with new and untraceable techniques for scamming genuine users. With the increasing impact of fraud, financial institutions must use machine learning tools for scam detection to provide safety and security to their users' accounts. This paper consists of an interdisciplinary comparative analysis of ML algorithms: Random Forest Classifier, XGBoost, ADABoost, Logistic Regression, Decision Trees, and Artificial Neural Networks. The diverse selection of algorithms aims to determine the most effective algorithm with the highest accuracy score for predicting legitimate and fraudulent transactions. The methodology involves fetching data from an Excel file, training the models, testing them on 20% of the dataset, and finally obtaining results in a Word document, having a confusion matrix and accuracy of each algorithm. By comparing the results of the confusion matrix and accuracy score, the Random Forest Classifier and XGBoost demonstrate the highest accuracy scores, while Logistic Regression yields a lower accuracy score. © 2024 IEEE.

关键词： Logistic regression

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Cognitive Frailty Classification Models for Older Adults in a Point-of-Care system

Cognitive Frailty Classification Models for Older Adults in ...

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IEEE Asia-Pacific Conference on Circuits and systems

作者： Lei Cao Yang Wei Lim Maw Pin Tan Fakhrul Zaman Rokhani Dept. of Computer and Communication System Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia Dept.of Medicine Faculty of Medicine University of Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

Cognitive Frailty (CF) is a prevalent condition among older individuals, combining physical frailty with cognitive impairment, with an increased risk of adverse health outcomes. However, the current diagnostic processes for CF are often hospital-based and not feasible for all patients, leading to a gap in timely and efficient diagnosis. To address this problem, a Point-of-Care (POC) system is proposed by utilizing Lab-on-a-Chip (LOC) technology and Tiny Machine Learning (TinyML) capabilities on Android devices for autonomous blood and urine sample collection and real-time CF assessment. In this paper, the feature selection technique for ML-based CF classification is also proposed by using several statistical test methods to improve the accuracy of the ML model. The results demonstrate significant improvements in the accuracy of CF classification through advanced machine learning models, achieving over 70% accuracy in key models such as Linear Discriminant Analysis (LDA), Gaussian Naive Bayes (GNB), and Support Vector Machines (SVM). This innovative system enhances accessibility and efficiency in diagnosing CF, benefiting older individuals in remote or resource-limited environments.

关键词： Support vector machines Analytical models Accuracy Tiny machine learning Point of care Gaussian processes Feature extraction Linear discriminant analysis Older adults Medical diagnostic imaging

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Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, Real-Time Approach with Angiography Imaging 5

Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, ...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Aruna Kumara, B. Gudodagi, Raveendra Madan, H.T. Kavitha, D.D. Manjunatha, H.M. Proudhadevaraya Instititute of Technology Dept. of Computer Science & Engg. Hosapete India School of Electronics and Communication Engg. REVA University Bengaluru India NMAM Institute of Technology Nitte India HKBK College of Engineering Dept. of Computer Science & Engg. Bengaluru India Bapuji Institute of Engineering and Technology Electrical & Electronics Engg. Davanagere India

ISBN: (纸本)9798331518097

Brain aneurysm is a swell or balloon in a blood vessel in the brain that can cause severe health problems to the patients. Due to its potentiality, if it ruptures it leads to serious neurological disorder such as: coma, hemorrhagic stroke, and even death. Early identification of brain aneurysm plays a crucial role in treatment. However, the traditional methods such as computerized Tomographic Angiography (CTA), Magnetic Resonance Imaging (MRI) takes more time and complex procedures. The relentless advancement of medical imaging technologies continues to transform diagnostic paradigms in neurology. In this context, this study integrates the convolutional neural networks (CNNs) into real-time brain aneurysm detection from angiography image. This paper presents a CNN-based model designed to rapidly and accurately identify cerebral aneurysms from angiography images. The proposed method, initially prepares the dataset by collecting diverse angiography images from medical archives. Later, the images undergoes with various preprocessing methods such as: denoising, contrast enhancement, normalization, resizing, and augmentation to enhance the quality of data, and model generalization. Then, the method automatically extracts the blood vessel structure and aneurysm characteristics such as: irregular shapes, unusual bulges, and densities within the vessels as features. Finally, the extracted features passed through the connected layers and the CNN classifies the whether an image contains aneurysm or not. The CNN model is trained with stochastic gradient descent (SGD) algorithm, and the trained model is deployed on a standard Graphics Processing Unit (GPU) for real time inference. The results showed that, the performance of the method was superior compared to the traditional methods. © 2024 IEEE.

关键词： computerized tomography

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Implementation of Multiple Discriminant Analysis (MDA) for Clustering Smart Village in West Java Based Podes (Potensi Desa) Database 4

Implementation of Multiple Discriminant Analysis (MDA) for C...

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4th International Conference on Informatics, Multimedia, Cyber and Information system, ICIMCIS 2022

作者： Tosida, Eneng Tita Solihin, Indra Permana Jayawinangun, Roni Ardiansyah, Deden Pakuan Univeristy Computer Science Dept. Bogor Indonesia Upn Veteran Informatic Dept. Jakarta Indonesia Pakuan University Communication Science Dept. Bogor Indonesia Pakuan University Computer Engineering Dept. Bogor Indonesia

ISBN: (纸本)9781665473279

Village development is accelerated through the smart village program. Village development in Indonesia, including West Java Province, is measured using the Developing Village Index (IDM). The 50 villages that are the locus of smart villages in West Java Province consist of three statuses, namely underdeveloped, developing and advanced. By using clustering using Multiple Dicriminant Analysis (MDA), this study try to identify smart village locus indicators based on the Village Building Index (IDM) based on Village Potential Data and classify them based on their status. The results of the smart village clustering in West Java province show that the smart village program can be adopted by villages with a lagging village index (IDM) level of 12%. Nine predictors were found to distinguish intelligent villages with lagging, developing and independent status, namely Public places, hospitals, pharmacies, community activities, security posts, banks & financial institutions, agricultural stores and Sewerage Treatment. This condition is a very interesting finding because it breaks the assumption that smart villages can only be adopted by villages with a minimally developed IDM level. The results of smart village clustering based on village potential data also show that smart village clusters are dominated by developing IDM-level villages. The multiple discriminant-based clustering model was carried out through two alternative models, and one of the models was able to show a canonical correlation of 0.906. © 2022 IEEE.

关键词： Discriminant analysis

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Energy Consumption for Cognitive Radio Network Enabled Multi-Access Edge Computing 3

Energy Consumption for Cognitive Radio Network Enabled Multi...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Elnaim, Areeg Ali Babeker, Amin Barakat, Mohammed Gaid, Abdulguddoos S.A. Saeed, Mamoon M. Ahmed, Zainab. E. Saeed, Rashid A. College of Engineering and Communication Alneelain University Khartoum Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Taiz University Dept. of Communication & Computer Engineering Taiz Yemen Faculty of Engineering Department of Communications and Electronics Engineering Yemen Gezira University Department of Electrical and Computer Engineering Khartoum Sudan College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia

ISBN: (纸本)9798350305333

This article discusses optimizing energy Consumption in CRN-enabled Multi-access Edge Computing (MEC) systems, presenting a novel approach to optimize the energy Consumption of a cognitive radio network-enabled MEC system using iterative optimization techniques. The proposed model aims to minimize the system's energy consumption while maintaining its performance and quality of service requirements. The iterative optimization algorithm is used to find the optimal allocation of resources, such as computing, communication, and power, among different nodes in the MEC system. As a result, the cognitive radio network enables efficient spectrum utilization by dynamically allocating available spectrum to different users based on their requirements. The performance evaluation approach is evaluated through simulations, and the results show that it can significantly improve the energy consumption of the MEC system while maintaining its performance and QoS requirements. This article contributes to developing sustainable and energy-efficient communication systems for future wireless networks. © 2023 IEEE.

关键词： Cognitive radio

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Modified feedback narrowband active noise control with online secondary-path modeling 24

Modified feedback narrowband active noise control with onlin...

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24th International Congress on Acoustics, ICA 2022

作者： Ma, Yaping Xiao, Yegui Ma, Liying Khorasani, Khashayar Dept. of Automation Jiangnan University China Dept. of Information and Management System Prefectural University of Hiroshima Japan Dept. of Electrical and Computer Engineering Concordia University Canada

Feedback narrowband active noise control (NANC, FBNANC) systems have been developed to suppress sinusoidal noise signals generated by rotating machines such as diesel engines, fans, etc. They do not require any reference sensor and can avoid the frequency mismatch and the acoustic feedback, as compared with the conventional feedforward NANC (FFNANC) systems. In this paper, a modified FBNANC system is proposed which contains three subsystems, namely: 1) a cascade pre-filtered and parallelly updated adaptive notch filter bank (CPANFB) that is included to synthesize the reference sinusoidal signals for the FBNANC controller, 2) an online secondary-path modeling (SPM, OSPM) subsystem that is introduced into the new system to track the secondary-path changes, and 3) a bandpass filter bank that is applied to the residual noise for separating the remaining sinusoids from other broadband noise components, with the former utilized to scale the auxiliary white Gaussian noise (AWGN) for the purpose of reducing its contribution to the residual noise. The three subsystems including the MPA (magnitude and phase adjuster) based controller are seamlessly integrated to work in harmony such that the whole FBNANC system enjoys nice advantages in both convergence and noise reduction performance (NRP). Extensive simulations are conducted to demonstrate and confirm the effectiveness of the proposed system. © ICA *** rights reserved

关键词： adaptive notch filter bank (ANFB) Feedback narrowband active noise control (FBNANC) magnitude noise reduction performance (NRP) online secondary-path modeling (OSPM) phase adjuster (MPA)

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Sleepwalking Detection and Prevention system using IoT

Sleepwalking Detection and Prevention System using IoT

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International Conference on ent Computing and Control systems (ICICCS)

作者： Krishna Prasad D S Shrinivasa Thrisha J Shetty K P Bhoomika Rai Dhanya T Kunder Dept. of Computer and Communication Engineering Nitte (Deemed to be University) NMAM Institute of Technology India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Sleepwalking is a parasomnia disorder that can lead to hazardous situations, including falls, wandering, and injuries. This study presents a novel Internet of Things (IoT)-based wearable system designed to detect and prevent sleepwalking incidents in real time. The system consists of a wearable device that continuously monitors movement using a pedometer sensor and a caregiver monitoring unit for remote alerts. An ESP32 microcontroller processes motion data, activates a mild electrical stimulus through a shock module to awaken the user, and transmits alerts via Bluetooth to notify caregivers. The system follows a threshold-based detection algorithm to identify sleepwalking behavior and trigger timely intervention. Experimental evaluations demonstrate the system’s reliability, low computational cost, and real-time responsiveness. However, practical limitations such as false positives, user discomfort, and Bluetooth range constraints must be addressed for broader adoption. Overall, the proposed system provides an efficient, real-time, and low-power solution for sleepwalking prevention, enhancing both user safety and caregiver awareness.

关键词： Bluetooth Microcontrollers Tracking Prevention and mitigation Real-time systems Safety Reliability Wearable devices Biomedical monitoring Monitoring

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