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Cardiac Anomaly Detection Using Machine learning 22nd

Cardiac Anomaly Detection Using Machine Learning

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Naseeba, B. Haranath, A. Prem Sai Pamarthi, Sasi Preetham Farook, S. Bhanu, B. Balaji Rao, B. Narendra Kumar VIT-AP University Amaravati India Department of EEE Sree Vidyanikethan Engineering College AP Tirupati India Department of Electronics Andhra Loyola College AP Vijayawada India Department of CSE Sree Vidyanikethan Engineering College AP Tirupati India

ISBN: (纸本)9783031274084

The heart is an essential organ in the human body. It can be attributed to birth problems, heredity, or may even be due to our health routine. Therefore, it has become exceedingly tough for healthcare practitioners to detect and anticipate cardiovascular problems at an early stage considering several criteria such as an aberrant pulse rate or excessive BP. Which has resulted in a desperate need for an effective and reliable technique to detect them, considerably expanding with each passing day. Five Machine learning algorithms Support Vector Machine, K-Nearest Neighbour, Logistic regression, Random Forest, and Artificial Neural Network are used to operate on massive datasets gathered from the healthcare industry, anticipating, and aiding in the decision-making process. The predicted outputs are based on features like Age, Blood Pressure rate, Cholesterol, Glucose, Smoke, Alcohol, etc. The features necessary for this model are basic enough for any ordinary non-medical individual to determine whether they are at risk for any cardiac disorders with a simple blood scan rather than a medical evaluation. We achieved an accuracy of 99.8% by using the K-NN algorithm which is greater than any of the previous articles. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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intelligent ECG-Based COVID-19 Diagnose Device Using CNN Deep learning Approaches 2nd

Intelligent ECG-Based COVID-19 Diagnose Device Using CNN Dee...

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2nd international conference on Advances in Computational Intelligence and Informatics, ICACII 2023

作者： Keshamoni, Kumar Rao, Koteswara Subba Rao, D. Koneru Lakshmaiah Education Foundation Hyderabad India Siddhartha Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819747269

The COVID-19 virus has caused millions of fatalities worldwide. As a component of our ongoing initiatives to curb the transmission of COVID-19, we are implementing the following measures;a quicker and safer diagnosis is highly desired. The analysis of electrocardiogram (ECG) data has shown usefulness for detecting cardiac disease, stroke, and COVID-19. We developed a fully automated procedure to detect COVID-19 infection in ECGs. Electrocardiographic (ECG) signal-based technologies and computerized models enable the precise identification of COVID-19 following substantial research into the process that the illness is going through, the symptoms that the patient is experiencing, and the laboratory diagnosis. The measures above (accuracy, precision, recall, FSCORE, and false prediction rate) showed excellent results. We discovered that the suggested models for COVID-19 detection with this cutting-edge technology worked well. Logistic regression, InceptionV3, KNN, decision tree, LSTM, SVM, ResNet, Naive Bayes and MobileNet were all equally efficient and precise for binary classification. Our results indicate the idea that it is possible to recognize COVID-19 in ECG images by utilizing deep learning has potential. This is a possibility. Our findings imply that COVID-19 can be recognized by a system that has been trained on digital ECG signals using deep learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Building NLP Tools to Process Sinhala Text data Written using English Letters 22

Building NLP Tools to Process Sinhala Text Data Written usin...

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22nd international conference on Advances in ICT for Emerging Regions (ICTer)

作者： Kurera, W. S. A. Rajapaksha, R. K. Rupasinghe, H. A. P. Liyanage, K. L. D. U. B. Kasthuriarachchi, Sanvitha Rajapakshe, Samantha SLIIT Fac Comp Malabe Sri Lanka

ISBN: (纸本)9798350346138

Sri Lanka is a country where the Sinhala and English languages take a prominent place, but most people are not so fluent in the two languages when using digital platforms like social media. Although most of the Sri Lankans use Singlish there is no proper application to translate the relevant language into the main two languages, Sinhala or English. The purpose of this research is to bring more attention of the researchers towards the sub varieties of Sinhala language, especially Singlish by bridging the gap between NLP tools available for processing textual data of main languages and their varieties. And each component focuses on translating Singlish text data into English, translating Singlish into Sinhala, sentiment analysis for Singlish text data and mapping emojis for Singlish tokens. There are language translation tools to translate many languages like Russian, Italian, Japanese into the English Language but there are no proper tools to translate the mainly used sublanguage in Sri Lanka;that is Singlish. Therefore, the proposed solution for the above problem is developing a Singlish to English Language Translation Model and removing slang words to allow NLP technologies created for English to be utilized to handle Singlish textual data. This NLP model shall have the capacity of translating Singlish words that are used on social media into English and if there are any slang words found during the translation, those words will be identified and removed. Translating the Singlish textual data into English would help the users to use other tools built to plagiarize, tools that are used to summarize and tools that are used to paraphrase English text.

关键词： Machine learning Natural Language Processing Sentiment analysis Feature engineering API facilities

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Physically Informed Graph-Based Deep Reasoning Net for Efficient Combinatorial Phase Mapping 22

Physically Informed Graph-Based Deep Reasoning Net for Effic...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Min, Yimeng Chang, Ming-Chiang Kong, Shufeng Gregoire, John M. Van Dover, R. Bruce Thompson, Michael O. Gomes, Carla P. Cornell University Materials Science and Engineering Ithaca United States Cornell University Computer Science Ithaca United States Sun Yat-sen University Software Engineering Guangzhou China California Institute of Technology Liquid Sunlight Alliance Pasadena United States

ISBN: (纸本)9798350345346

Phase mapping is a crucial challenge in materials discovery, which entails determining crystalline phase distribution in condition space based on a collection of X-ray diffraction (XRD) data. This task involves exploring the space of potential phases, identifying existing phases, and determining their respective weight distribution in the condition space while adhering to strict physics constraints. In recent years, there has been a growing interest in leveraging machine learning (ML) techniques to tackle the phase mapping problem. ML methods offer the potential to handle larger and more complex phase mapping instances and provide enhanced accuracy compared to traditional approaches. Among promising ML approaches, DRNets, which formulates the phase mapping problem as an unsupervised pattern demixing problem, represents the current state of the art. Despite its practical effectiveness, DRNets does have certain limitations. For instance, it employs a single multiplicative factor to calculate the stick locations in XRD patterns, which may not accurately reflect the underlying physics of X-ray diffraction. Additionally, DRNets relies on an expensive path-based schema to enforce phase weight smoothness. To overcome these limitations, we propose a novel approach called Physically-informed Graph-based DRNet (PG-DRNet). PG-DRNet incorporates a physical decoder that estimates the crystals' lattice parameters and reconstructs XRD patterns based on Bragg's law. Additionally, we introduce a graph-based schema to enforce phase weight smoothness as well as lattice and peak intensity shift. This graph-based schema provides several advan-tages, including improved computational efficiency compared to the path-based schema utilized in DRNets. To thoroughly evaluate the effectiveness of our approach, we conducted experiments on various chemical systems. Notably, our evaluation went beyond the scope of previous studies that solely focused on varying compositions and extends to explore

关键词： graph-based smoothness phase mapping physically informed decoding unsupervised pattern demixing

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VANET Handoff from IEEE 80.11p to Cellular Network Based on Discharging with Handover Pronouncement Based on Software Defined Network (DHP-SDN) 22nd

VANET Handoff from IEEE 80.11p to Cellular Network Based on ...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Sarvavnan, M. Narayanan, R. Lakshmi Kavitha, K. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (纸本)9783031274084

In Vehicular Adhoc Network (VANET) is an emerging domain with high dynamic mobility and disrupted network in Discharging with Handover Pronouncement based on Software Defined Network (DHP-SDN) with advance predictive management tool described in this work for discharging of vehicle-to-infrastructure communication with Software Defined Network (SDN) architecture. Various parameters were addressed in research articles like maintaining connectivity, identifying appropriate intermediate node for carrying signal and transferring of data but in the proposed model SDN monitors the discharging signal based on the speediness, terrestrial location, nearby vehicle RSU from the vehicle which has cellular and IEEE 802.11p network boundary. SDN regulator computes when the due time is to settle on a choice, chooses whether it is appropriate or not have the vehicle to handoff from cell organization to the ahead IEEE 802.11p organization. The simulation results that using DHP-SDN method maintains the networking quality by reducing the load and traffic congestion. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicles

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Erasable-Itemset Mining for Sequential Product databases 22nd

Erasable-Itemset Mining for Sequential Product Databases

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Hong, Tzung-Pei Chen, Yi-Li Huang, Wei-Ming Tsai, Yu-Chuan Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Electrical and Control China Steel Inc. Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9783031274084

Erasable-itemset mining has become a popular research topic and is usually used for product production planning in the industry. If some products in a factory may be removed without critically affecting production profits, the set composed of them is called an erasable itemset. Erasable-itemset mining is to find all the removable material sets for saving funds. This paper extends the concept of erasable itemsets to consider customer behavior with a sequence of orders. We consider the scenario that when an item (material) is not purchased, a product using that material cannot be manufactured, and clients will cancel all their orders if at least one such order exists. We propose a modified erasable-itemset mining algorithm for solving the above problem. Finally, experiments with varying thresholds are conducted to evaluate the execution time and mining results of the proposed algorithm considering customer behavior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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A Hybrid Approach for Predicting Bitcoin Price Using Bi-LSTM and Bi-RNN Based Neural Network 22nd

A Hybrid Approach for Predicting Bitcoin Price Using Bi-LSTM...

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22nd international conference on intelligent data engineering and automated learning

作者： Das, Sunanda Billah, Md Masum Mumu, Suraiya Akter Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna 9203 Bangladesh

ISBN: (纸本)9783030916077;9783030916084

Bitcoin is an electronic or digital currency. However, unlike government-issued currencies, there is no single entity that issues bitcoin or is in charge of processing transactions. That's why bitcoin has become popular in the recent era. As bitcoin's price fluctuates a lot in a short period, it is very challenging to predict the bitcoin price accurately. In this paper, we proposed a method by merging different highest-level building blocks in deep learning such as Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), Bi-LSTM, Recurrent Neural Network (RNN), and Bi-RNN to predict bitcoin price as accurately as possible. Though CNN, LSTM, Bi-LSTM, RNN, Bi-RNN or ARIMA independently produce an acceptable result, our proposed hybrid method is fairly reliable compared to individual building block of the network as the proposed method outperforms other individual models and achieves RMSE, MAE, MAPE, MedAE of 2.69%, 1.78%, 2.20%, and 1.23% respectively.

关键词： Bitcoin Prediction Deep learning CNN LSTM Bi-LSTM RNN Bi-RNN Time series forecasting

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2nd international conference on Pan-African Intelligence and Smart Systems, PAAISS 2022

2nd International Conference on Pan-African Intelligence and...

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2nd international conference on Pan-African Intelligence and Smart Systems, PAAISS 2022

ISBN: (纸本)9783031252709

The proceedings contain 27 papers. The special focus in this conference is on Pan-African Intelligence and Smart Systems. The topics include: Help in the Early Diagnosis of Liver Cirrhosis Using a learning Transfer Method;Automatic Detection of COVID-19 Using Ensemble Transfer learning Based on Lung CT Scans;pancreas Instance Segmentation Using Deep learning Techniques;automating Sickle Cell Counting Using Object Detection Techniques;quaternionic Wavelets for Estimating Forest Biomass by Gradient Boosting Methods;detection and Classification of Underwater Acoustic Events;plant Diseases Detection and Classification Using Deep Transfer learning;light-Weight Deep learning Framework for automated Remote Sensing Images Classification;deepMalOb: Deep Detection of Obfuscated Android Malware;reinforcement learning-Based Dynamic Path Allocation in IoT Systems;reconfigurable intelligent Surfaces: Redefining the Entirety of Wireless Communication Systems in Leaps and Bounds;A New Class of DC-Free Run-Length Limited Codes;conversational Pattern Mining Using Motif Detection;facial Expression Recognition with Manifold learning and Graph Convolutional Network;speech Emotion Classification: A Survey of the State-of-the-Art;forward Obstacle Detection by Unmanned Aerial Vehicles;multi-agent Reinforcement learning Based Approach for Vehicle Routing Problem;an On-Site Collaborative Approach of Road Crash data Collection;reduction of data Transmission in an IoT Wireless Sensor Network;a Comparative Study of Regressors and Stacked Ensemble Model for Daily Temperature Forecasting: A Case Study of Senegal;exploring Use of Machine learning Regressors for Daily Rainfall Prediction in the Sahel Region: A Case Study of Matam, Senegal;dynamic Pre-trained Models Layer Selection Using Filter-Weights Cosine Similarity;learning Approximate Invariance Requires Far Fewer data;deep Matrix Factorization for Multi-view Clustering Using Density-Based Preprocessing;Application of Genetic Algorithm for Com

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A Multi-source Domain Generalization Network for Rotating Machinery Fault Diagnosis under Unseen Operating Conditions 22

A Multi-source Domain Generalization Network for Rotating Ma...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Gao, Tianyu Yang, Jingli Hao, Weiwei Fan, Xiaopeng School of Electronics and Information Engineering Harbin Institute of Technology Harbin China China Institute of Marine Technology and Economy Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798331527471

With the rapid development of industrial intelligence, the domain adaptation technology depending on the availability of the target domain has gradually become a reliable solution to address the performance degradation of fault diagnosis due to the variation of operating conditions for rotating machinery. However, the fault diagnosis scenarios under unseen operating conditions are commonly confronted in practical engineering. In response to the above limitations, a novel multi-source domain generalization network (MDGN) is proposed in this paper to enhance the fault diagnosis performance of rotating machinery under unseen operating conditions by decoupling the domain-relevant feature information for obtaining domain-invariant diagnosis knowledge. First, a temporal convolutional autoencoder (TCAE) is developed as a feature extraction module to fully capture the effective spatio-temporal information of the sample data. Then, a domain classification module is designed for adversarial transfer learning to facilitate the acquisition of domain-invariant features. Finally, domain divergence loss and boundary margin loss are constructed for MDGN to supervise the feature distribution alignment among domains and feature distinction among classes. The mechanical comprehensive diagnosis simulation platform (MCDSP) bearing dataset collected in our laboratory is employed to evaluate the domain generalization performance of the proposed method. The experimental results demonstrate that the method achieves an average fault diagnosis accuracy of 94.50%, which is better than the comparison algorithms. © 2024 IEEE.

关键词： Rotating machinery

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Exploiting Hierarchical Correlations for Cross-City Cross-Mode Traffic Flow Prediction 22

Exploiting Hierarchical Correlations for Cross-City Cross-Mo...

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22nd IEEE international conference on data Mining (ICDM)

作者： Chen, Yan Gu, Jingjing Zhuang, Fuzhen Lu, Xinjiang Sun, Ming Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Chinese Acad Sc Key Lab Intelligent Informat Proc Beijing Peoples R China Baidu Research Business Intelligence Lab Beijing Peoples R China

ISBN: (纸本)9781665450997

As a promising learning paradigm for addressing the data scarcity and distribution mismatch issues, cross-domain prediction aims to leverage the transferable knowledge from the source domain to solve the learning problems in the target domain. Indeed, many urban computing tasks, such as crosscity/mode traffic flow prediction, have to face the severe data scarcity problem due to the heterogeneity in different data sources as well as the imbalanced development among cities. To this end, in this paper, we propose a cross-domain learning framework, namely CCMHC, which exploits Hierarchical Correlation between domains for Cross-City cross-Mode traffic flow prediction. Specifically, we first measure the correlation among inter-city traffic flows by exploring the similarity of region functions and road-networks. In this step, we filter out the regions with lower transfer ability from the source city to the target city. Then, we calculate the temporal correlations of traffic flows across different modes to select a source region that is highly related to the target region in a dynamic way. Moreover, a cross-domain urban flow prediction method is devised by transferring shared knowledge from the source city to the target city. Finally, experimental results on real-world data demonstrate the superiority of CCMHC over the state-of-the-art transfer learning methods. In addition, the generalization ability of the CCMHC framework on different neural network-based models is also validated.

关键词： Flow Prediction Cross-City Cross-Mode

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