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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Proof of Equation: A Novel Consensus Algorithm for Dynamic Spectrum Access 16

Proof of Equation: A Novel Consensus Algorithm for Dynamic S...

引用

16th IEEE AFRICON, AFRICON 2023

作者： Silva, Thiwanka Bamunuge, Madhushika Dissanayake, Dilusha Seneviratne, Chatura Gamage, Tharindu Liyanage, Madhusanka University of Ruhuna Faculty of Engineering Dept. of Electrical and Information Eng. Sri Lanka School of Computer Science University College of Dublin Ireland

ISBN: (纸本)9798350336214

The future of communication technology is moving from 5G to 6G with new innovations. Blockchain (BC) is a such immersive technology that significantly impacts the betterment of communication technology. BC-based spectrum-sharing solutions can be used in Dynamic Spectrum Access (DSA) systems to fulfill the need for secure and efficient communication. With the invention of cognitive radio networks, DSA became a popular topic for the scientific community. Spectrum misuse/violations can occur due to the rapid growth of spectrum sharing. As the system is open to malicious attacks, licensed spectrum owners must be identified and verified. However, the existing BC-based DSA solutions are more expensive, non-optimized, and lack spectrum misuse detection. This paper proposes a novel consensus algorithm called 'Proof of Equation' for spectrum misuse detection. The core of the proposed algorithm is a consensus score calculation based on a numerical equation with three parameters rather than using cryptographic calculations. The performance of the proposed algorithm is studied using Python simulations, and simulation results show that the proposed algorithm outperforms the Proof of Work (PoW) and Proof of Stake (PoS) consensus algorithms in terms of block production time. © 2023 IEEE.

关键词： Smart contract

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Indian Sign Language Recognition Using Scale-Invariant Feature Transform by dept. Sensor 5

Indian Sign Language Recognition Using Scale-Invariant Featu...

引用

5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Gunjan, Vinit Kumar Manipal University Jaipur Dept. of Artificial Intelligence & Machine Learning Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

Deaf and hard of hearing people in the Indian community now have a way to communicate with society because to Indian Sign Language (ISL). The hand is accorded significant importance in the framework of the ISL. ISL is composed of both static and dynamic sign hand gestures. Both static and dynamic signs are made up of three components: hand movement, hand orientation, and hand position. The proposed method can recognize static ISL signs. The technique that was suggested makes use of the Microsoft Kinect sensor to locate and separate a hand from its background in a complex scene. SIFT, which stands for 'scale-invariant feature transform,' was used to extract the feature. The SIFT algorithm is invariant under changes in perspective, orientation, and lighting. Recognize the 10 ISL digit movements with an accuracy of 94.9% using a multi support vector machine (MSVM). © 2023 IEEE.

关键词： Support vector machines

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Indian Food Segmentation and Calorie Estimation Using CatBoost and Masked Convolutional Neural Networks

Indian Food Segmentation and Calorie Estimation Using CatBoo...

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2023 IEEE International Conference on Network, Multimedia and information Technology, NMITCON 2023

作者： Acharya, Nabin Singh, Ayush Kumar Dwivedi, Anshuman Kumar Jayanthi, M.G. Kannadaguli, Prashant Cambridge Institute of Technology Dept. of Information Science and Engineering Bengaluru India Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (纸本)9798350300826

Accurate food segmentation and calorie estimation play a pivotal role in effective dietary monitoring and health management. In this paper, after extensive experimentation on a diverse Indian food dataset of 20 classes collected by us, we present two separate innovative models designed to address these tasks with exceptional precision. The first model employs a machine learning approach by incorporating the CatBoost algorithm, while the second model leverages a deep learning technique utilizing the U-Net architecture for high-quality image segmentation. Additionally, we merge the U-Net outcomes with a convolutional neural network (CNN) to enhance the deep learning-based classification. Our models effectively handle categorical features and address imbalanced data, resulting in significantly improved accuracy in food item delineation within images. This advancement enables a more reliable and comprehensive analysis of dietary patterns. We demonstrate the effectiveness and robustness of our proposed models. Comparative evaluations against state-of-the-art methods verify exceptional performance in terms of segmentation accuracy, precision in food classification, and estimation of calorie intake. Notably, our models are specifically tailored for food images and do not take ingredients or any other information into consideration. In comparison CNN model is performs better than CatBoost, achieving an accuracy of 78%. © 2023 IEEE.

关键词： Convolutional neural networks

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection Systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

引用

14th International Conference on information and Communication Systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

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Video key concept extraction using Convolution Neural Network 3

Video key concept extraction using Convolution Neural Networ...

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3rd IEEE International Conference on AI in Cybersecurity, ICAIC 2024

作者： Sardar, Tanvir H Amin Hazarika, Ruhul Pandey, Bishwajeet Guru Prasad, M.S. Mahmudul Hassan, Sk Dodmane, Radhakrishna Gohel, Hardik Dept. of Computer Science & Engg Bengaluru India Manipal Institute of Technology Department of Information Technology Bengaluru India Astana It University Department of Intelligent System and Cyber Security Kazakhstan Dept. of Computer Science & Engg. Dehradun India VIT-AP School of Computer Science and Engineering India Nmam Institute of Technology Department of Computer Science & Engg. Nitte India University of Houston Department of Cse Victoria United States

ISBN: (纸本)9798350381856

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video. © 2024 IEEE.

关键词： Speech recognition

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Lung Nodule Segmentation and Classification Using Conv-Unet Based Deep Learning

Lung Nodule Segmentation and Classification Using Conv-Unet ...

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2023 IEEE International Conference on Network, Multimedia and information Technology, NMITCON 2023

作者： Akash, V. Monish Sai, R. Amith, K.B. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bangalore India Cambridge Institute of Technology Dept. of Information Science and Engineering Bangalore India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bangalore India Dhaarini Academy of Technical Education Bangalore India

ISBN: (纸本)9798350300826

This paper proposes a Convolutional U-Net architecture, a variation of the standard U-Net architecture for the segmentation of lung nodules and classification using Deep learning on computerized Tomography (CT) scans. The Primary steps employed are Preprocessing, Segmentation and Classification of nodules. In the preprocessing step, the lung region is segmented using techniques such as normalization, median filtering, Kmeans clustering, morphological and thresholding operations to extract lung Region of Interest (ROI) and nodule masks. The Conv-Unet design adds more convolutional layers to the standard U -Net architecture to help capture complicated patterns and boundaries of lung nodules for more accurate segmentation. Categorization of the segmented lung nodules is done using a CNN network on the LIDC-IDRI, and LUNA16 dataset. Overall, this model achieves a dice score of 62% and classification accuracy of 82% displaying appropriate performance in comparison with other variations of the U-Net architecture. © 2023 IEEE.

关键词： Convolution

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Research on Images Recognition based on Deep Convolution Neural Network 8

Research on Images Recognition based on Deep Convolution Neu...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Chang, Xiaobo Zhao, Linlin Wang, Jinxiang Yanbian University College of Engineering Yanji China Yanbian University Intelligent Information Processing Lab. Dept. of Computer Science & Technology Yanji China College of Engineering Yanbian University Computer Science and Technology Yanji China

ISBN: (纸本)9798350302455

Image recognition has become a necessary component for computer visual system and widely utilized to detect objectives for downstream tasks in realistic applications. However, existing methods are concentrated on utilizing the clustering information of image features to recognize the subjects, which are unable to dispose several high correlation subjects and cost numerous computation period. In this paper, we utilize the convolution operation for images and extract the separated features. After acquiring these features, a deep neural network is established to recognize the objectives in the input images with enough iterations training procedures. Subsequently, the trained model is evaluated through the testing data-set to measure the real performance of proposed method. From our extensive experimental results, we can conclude that our proposed model can automatically realize the recognition process for input images with reasonable accuracy and acceptable computation costs. Additionally, our experimental results also indicate that the convolutional operation is more suitable to dispose the images data-set than traditional machine learning method. © 2023 IEEE.

关键词： Convolution

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10-point connector formed by two snap buttons

10-point connector formed by two snap buttons

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Kao, Rally Liao, Kuo-Cheng Wu, Ze-We Chen, Shih-Hung Chen, Chi-Chun Ming Young Biomedical Corp Miaoli Taiwan National Chin-Yi University of Technology Program Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan Bonanza Biotechnology Co.Ltd National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

With the rapid advancement of electronic textiles, the design and integration of connectors have emerged as pivotal challenges. This study emphasizes the development of a connection solution that amalgamates miniaturization, efficiency, and reliability, specifically tailored for Flexible Conductive Silicone Circuits. A diversified connection scheme is proposed, incorporating elements such as snaps, Pogo pins, and conductive hook-and-loop fasteners, thereby achieving an innovative and pragmatic design. Through meticulous manufacturing processes, including the utilization of hot-melt adhesive, PET films, conductive fabric, and other multilayer materials, a connection system with low impedance and commendable conductivity was successfully engineered. Impedance test outcomes have indicated that the resistance at each connection point is less than 0.7 ohms, substantiating its superior electrical properties. In terms of assembly, this research demonstrates the precise interconnection of the controller and conductive fabric circuit, ensured by metal pin technology, leading to connection stability. This solution not only furnishes appropriate connection technology for specific application scenarios but also opens new avenues and directions for connection design within the domain of electronic textiles. © 2024 IEEE.

关键词： Connectors E-Textiles Flexible Conductive Silicone Circuit PET films

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A Framework for Structural Damage detection system in automobiles for flexible Insurance claim using IOT and Machine Learning

A Framework for Structural Damage detection system in automo...

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2022 International Mobile and Embedded Technology Conference, MECON 2022

作者： Agarwal, Deepshikha Tripathi, Khushboo Indian Institute of Information Technology Dept. of Information Technology Lucknow India Amity University Dept. of Computer Science and Engineering Haryana Gurgaon India

ISBN: (纸本)9781665420204

Automobiles are required in everyday life to commute from destination to other. But, due to faulty road conditions, heavy traffic and lack of adequate driver skills, crash incidents happen almost every hour of the day. With every vehicle, companies offer insurance which people can claim to get the damaged parts recovered. However, during the recovery, it solely depends on the insurance companies investigating officer to give an adequate claim to the vehicle owner. Due to inadequate visual precipitance, at times, the owner is not given the full insurance claim. The proposed framework offers automated damage detection system by which better and correct claim can be given to such an owner. The proposed framework is based on Machine Learning and IOT approach for transport network scenario. This paper presents the idea of implementing proposed framework to the researchers in future to detect damages and calculate the correct claim to the owner for Insurance claim. © 2022 IEEE.

关键词： Automobiles

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