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IECC 2024 - 2024 6th international Electronics Communication conference

IECC 2024 - 2024 6th International Electronics Communication...

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6th international Electronics Communication conference, IECC 2024

ISBN: (纸本)9798400717598

the proceedings contain 15 papers. the topics discussed include: performance variation caused by sprite drawing pattern for high-level synthesized sprite drawing hardware;hardware implementation of calibration data loading in device driver for an SPI peripheral;an effectual image based authentication scheme for mobile device using machine learning;cost-effective alternatives for squarer in Pan–Tompkins algorithm;development of sprite drawing hardware combining high-level synthesis and FPGA internal memory;threshold based (t, n) secrecy using open channel and prevent man-in-the-middle attack and share forgery: secrecy and prevent forgery;and a decentralized V2V driving safety mutual assistance mechanism based on blockchain and gossip communication protocol.

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Robust Heart Disease Prognosis: Integrating Extended Isolation Forest Outlier Detection with Advanced Prediction Models 6th

Robust Heart Disease Prognosis: Integrating Extended Isolati...

引用

6th international conference on Soft Computing and data mining (SCDM)

作者： Javid, Irfan Hassan, Norlida Ghazali, Rozaida Hassim, Yana Mazwin Mohmad Batool, Tuba Husaini, Noor Aida Jafri, Syed Irteza Hussain Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Johor Baharu Malaysia Univ Poonch Rawalakot Dept Comp Sci & IT Rawalakot Pakistan

ISBN: (纸本)9783031669644;9783031669651

Heart disease can be prevented with an accurate prognosis, but it can also be catastrophic if the forecast is erroneous. this paper presents an innovative approach for the prediction of heart disease by using machine learning and deep learning techniques with Extended Isolation Forest outlier detection. the proposed system leverages a dataset comprising various clinical features and diagnostic parameters associated with heart disease. Initially, the extended isolation forest algorithm is employed to identify outliers and mitigate their influence on subsequent analyses. this preprocessing step enhances the overall robustness of the prediction system. Next, machine learning and deep learning models is utilized for the prediction of heart disease. Multiple classification algorithms, including logistic regression, support vector machine, random forest and light gradient boosting are trained on the preprocessed dataset to identify patterns and relationships between the features and disease outcomes. Furthermore, a deep learning model, such as gated recurrent unit is employed to extract intricate patterns from the input data and capture temporal dependencies. the accuracy and confusion matrix is used to validate several promising outcomes. the integration of outlier detection techniques further enhances the system's performance by minimizing the impact of erroneous data, and also the data has been standardized to achieve optimal results. the accuracy of 93.4% was achieved using a deep learning method.

关键词： machine learning Deep learning Heart Disease Extended Isolation Forest Confusion Matrix

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A NLP-based method for extracting temporal dimension features from unstructured high-dimensional big data 6

A NLP-based method for extracting temporal dimension feature...

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6th international conference on Next Generation data-Driven Networks, NGDN 2024

作者： Wei, Peiyi Hangzhou Hikvision Digital Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9798350388374

With the advent of the era of big data, unstructured text data has exploded, so it is of great significance to extract effective time dimension features from unstructured high-dimensional big data. therefore, a time dimension feature extraction method of unstructured high-dimensional big data based on NLP is proposed. through the NLP deep learning model, combined with association rule mining technology and feature quantization methods, effective feature extraction optimization was carried out on the time dimension of unstructured multidimensional big data. Apply parallel data stream reconstruction and fuzzy C clustering algorithm to search for high-dimensional big data and arrange information. Based on a spatial framework, optimization of feature extraction from a temporal perspective in unstructured high-dimensional big data analysis has been achieved. Experimental results show that the signal-to-noise ratio of this method is -0.5dB~0.5dB, and the recognition accuracy is 99%. this proves that this method can not only accurately extract the time dimension features in the text, but also provide strong support for the subsequent text analysis tasks. © 2024 IEEE.

关键词： Spatio-temporal data

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Todly: Multimodal Approach for Toddler Safety and Well-Being Using Advanced machine learning 6

Todly: Multimodal Approach for Toddler Safety and Well-Being...

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6th international conference on Advancements in Computing, ICAC 2024

作者： Dewmini, Ashani Shashindi, Maleesha Uththara, Kaveen Sathushka, Gehan Manathunga, Kalpani Karunathilaka, Supipi Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

this study presents 'Todly,' an intelligent toddler monitoring system designed as a mobile application to ensure the safety and well-being of toddlers through cutting-edge technology. 'Todly' integrates advanced machine learning algorithms and neural networks to enhance child safety and provide caregiver support. this approach features face emotion and gesture detection, danger object detection, crying pattern analysis, and a chatbot assistant for new mothers. these components work together seamlessly to offer caregivers real-time insights into a child's emotional state, identify potential hazards, understand the reasons behind a toddler's crying, and provide timely support and guidance. the architecture is designed to be scalable and adaptable, making it suitable for various environments, such as homes, daycare centers, and pediatric healthcare facilities. Its user-friendly interface ensures accessibility for users with different levels of technical expertise, while the robust support infrastructure facilitates smooth operation and maintenance. the integrated solution demonstrated strong performance across its various components, achieving an accuracy of 89.9% in object detection, 85% in emotion detection, and 90.8% in cry analysis, encompassing all cry types. the chatbot's mT5 model also achieved BLEU scores of 38.7 for Sinhala-to-English translation and 31.2 for English-to-Sinhala translation. © 2024 IEEE.

关键词： Gesture recognition

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Enhancing Cardiac Health: machine learning in Coronary Artery Disease Prediction 6th

Enhancing Cardiac Health: Machine Learning in Coronary Arter...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Vijayaraj, Anu Ragavi Pasupathi, Subbulakshmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819780891

the alarming surge in cardiovascular diseases, with a particular focus on Coronary Artery Disease (CAD), is causing premature fatalities. this escalation is exacerbating the inefficiency of the diagnostic process, burdened by both time constraints and the strain on human resources, as more patients endure extended wait times for diagnosis. Addressing this pressing issue calls for the fusion of analytical decision-making with digital patient data, aiming to develop a robust and efficient predictive model for early-stage cardiac illness. machine learning (ML) emerges as a promising tool to bolster CAD detection and prognosis within the healthcare sector. this chapter introduces an ML-based approach for CAD prediction, leveraging the Z-Alizadeh Sani dataset sourced from the UCI repository. the classification task is tackled using three distinct ML models—Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). the findings underscore the superior performance of ANN, achieving an impressive 89.01 percent accuracy in CAD diagnosis. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cardiology

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A Continual learning approach based on Gaussian process model 6

A Continual learning approach based on Gaussian process mode...

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6th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wen, Zhaoyang Yao, Changsong Sheng, Liuhao Chen, Shiyu Cao, Qian Cui, Jiabao College of Intelligent Science National University of Defense Technology Hunan Province Changsha China College of System Engineering National University of Defense Technology Hunan Province Changsha China

ISBN: (纸本)9798331541699

In light of the limitations associated with traditional continual learning methods, such as catastrophic forgetting and inadequate generalization capabilities, we propose a new continual learning model guided by Gaussian Processes. this method constructs a relationship between batch data and the global distribution using a Gaussian model. By integrating Gaussian Processes into the empirical replay mechanism, we enhance the global awareness of batch data. the learning process involves modeling the mapping relationship between the feature and label spaces. We utilize Gaussian process regression to compute the contextual prediction probability for each sample within the current batch. By exploring the joint optimization of contextual prediction probabilities, original labels, and network prediction probabilities, we improve the representation learning capability of the neural network model, thereby enhancing the accuracy of target recognition over time. Experimental results demonstrate that our proposed method significantly reduces training and validation losses across MNIST, CIFAR10, and CIFAR100 datasets. the integration of Gaussian Processes markedly improves both the convergence speed and final accuracy of the model. these findings underscore the potential of Gaussian Processes in the training of complex neural networks and offer a new avenue for future continual learning research. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Broad learning Approach for Automatic Dependent Surveillance-Broadcast System 6

Efficient Broad Learning Approach for Automatic Dependent Su...

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6th international conference on Next Generation data-Driven Networks, NGDN 2024

作者： Hu, Chenhan Hu, Lifan Ding, Xiaxia Zhang, Yibin Peng, Yang University of Electronic Science and Technology of China Glasgow College Chengdu China Njupt College of Telecommunications and Information Engineering Nanjing China

ISBN: (纸本)9798350388374

Intelligent signal sensing and recognition, leveraging machine learning and signal processing techniques, is a promising approach for classifying digitally modulated signals in wireless communications. Accurately identifying and classifying various modulations are crucial for efficient signal identification, multi- user access, and spectrum allocation. Traditional methods typically depend on custom feature extraction and manually chosen classifiers, but these may falter in complex communication scenarios. Unlike conventional deep neural networks, which use a multi-layered architecture with numerous neurons per layer for classification tasks, broad neural networks adopt a more streamlined structure with fewer neurons per layer. this design significantly reduces the model's parameter count, thus lowering complexity and the risk of overfitting. Specifically, in automatic modulation classification tasks, broad neural networks serve as effective classifiers for recognizing and categorizing different modulations. these networks can adeptly parse and interpret varied modulated signals by fine-tuning the network's structure and parameters. © 2024 IEEE.

关键词： Signal modulation

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Feature Extraction and Classification of Respiratory Sound and Lung Diseases 6

Feature Extraction and Classification of Respiratory Sound a...

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6th international conference on pattern recognition and Image Analysis (IPRIA)

作者： Latifi, Seyed Amir Ghassemian, Hassan Imani, Maryam Tarbiat Modares Univ Image Proc & Informat Anal Lab Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9798350314557

Bacteria, viruses, and fungi can cause respiratory infections. It is usually possible to detect respiratory diseases early by listening to the lung sounds with a stethoscope. In reality, lung sound analysis is a time-consuming and difficult task that depends on medical skills and recognition experience. Recent advances in automatic respiratory sound recognition and classification have attracted more attention. the outbreak of COVID-19 throughout the world and the high patient numbers have placed a great deal of pressure on medical professionals. A smart algorithm is therefore a necessity to provide a faster and more accurate detection of lung infections by automatically processing the sounds of the lungs. this paper proposes two new lung sound feature extraction, maximum entropy Gabor filter bank (MAGFB), and maximum entropy Mel filter bank (MAMFB). the classification is performed by a deep neural convolution network (DCNN) by using 50% of data for training the classifier. the filter banks have been substituted, instead of the convolutional layers. Experiments were conducted on the ICBHI 2017 Challenge dataset (with eight classes). the proposed method has a better performance compared to famous methods such as MFCC and Wavelet transform. Particularly, the performance of the second method is significant. For ICBHI 2017 challenge dataset, the overall accuracy of MFCC, Wavelet, MAGFB and MAMFB were 87%, 86%,90% and 93%, respectively.

关键词： Filter bank Feature extraction Classification Deep learning Respiratory sounds lung diseases

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Semantic Segmentation of RGB Satellite Urban Images Based on Improved U-Net 6

Semantic Segmentation of RGB Satellite Urban Images Based on...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Liu, Siyong Zhao, Yili Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350325485

With the increase of satellite data in recent years, we can perceive the detailed structure of the Earth's surface from it, and obtaining this information opens up new directions for remote sensing image analysis. Generating high-quality building segmentation from RGB satellite images can well predict the geometry of cities, while significant progress has been made in semantic segmentation of satellite images based on deep learning, but most existing methods tend to have poor predictions on segmentation boundaries and poor separation of targets in contact with each other in the same category. In this paper, we propose an improved network based on U-Net by incorporating the SimAM parameter-free attention mechanism. In addition, we solve the problem of irregular contours of building segmentation in satellite images by generating weight mappings with a priori information to be added to the calculation of the loss function. We use a competition dataset named Building Missing Maps with machine learning on Aicrowed for training and prediction, and demonstrate that our method has good prediction results on the contours of building semantic segmentation independent of the size of satellite images and the number of datasets. © 2023 IEEE.

关键词： Satellite imagery

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TI-Fusion: A Multimodal Anxiety Disorder Detection Method 6

TI-Fusion: A Multimodal Anxiety Disorder Detection Method

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6th international conference on Electrical Information and Communication Technology, EICT 2023

作者： Shadid, Mahir Afnan, Mushfiqus Salehin Patwary, Muhammed J. A. International Islamic University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798350370836

Anxiety disorder significantly impacts individuals' well-being and daily functioning, emphasizing the need for early detection and accurate diagnosis. However, relying solely on unimodal data for machine learning approaches can produce misleading or inaccurate results. To address this issue, this paper presents a non-intrusive late multimodal fusion technique that integrates text and image data, resulting in a highly accurate unified output. Six different machine learning algorithms are utilized, ideally suited for analyzing the textual data within the particular dataset (DASS-21). these algorithms excel at extracting meaningful patterns, classifying and making accurate predictions based on textual data. Simultaneously, image data (KDEF, CK+) is processed using a Convolutional-Neural-Network (CNN) equipped with real Gabor-filter. CNN effectively captures texture, edges, and complex visual patterns, enabling precise image analysis and recognition tasks. A late multimodal fusion algorithm (Text-Image Fusion) is employed to combine the outputs from both modalities. this approach achieved a final accuracy of 97.62%. © 2023 IEEE.

关键词： machine learning

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