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MLCGAN: Multi-Lead ECG Synthesis with Multi Label Conditional Generative Adversarial Network

MLCGAN: Multi-Lead ECG Synthesis with Multi Label Conditiona...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jian Wu Liping Wang Hailin Pan Binyu Wang Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

Electrocardiography(ECG) is a non-invasive tool used to identify the cardiovascular diseases. ECG classification studies have been concerned and made progress well. However, the problems about categories imbalance and absence of labelled clinic data are still dramatically hindered research development. Recently, generative models have been verified as a possible way to handle the data scarcity issues. For ECG synthesis, to the best of our knowledge as the reason of time sequences and multiple labels constraints, no model can generate ECG corresponding to clinic *** this paper, we present a novel multi-label conditional generative adversarial network, named MLCGAN. To synthesise reasonable long-term multi-lead data, multi-label mixing module is devised to combine with our improved WaveGAN. Moreover, the sampling strategy based on multilabels distribution is proposed. Comprehensive experiments demonstrate that MLCGAN can generate ECG data satisfied the clinic diagnose requirement and improve the performance of RestNet based ECG classifier.

关键词： Knowledge engineering Electrocardiography Signal processing Generative adversarial networks data models Acoustics Cardiovascular diseases

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EmpTurnoverML: An Efficient Model for Employee Turnover and Customer Churn Prediction Using Machine Learning algorithms

EmpTurnoverML: An Efficient Model for Employee Turnover and ...

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Mobile, Intelligent, and Ubiquitous computing conference (MIUCC), international

作者： Diaa Salama AbdElminaam Mariam Maged Mariam Khaled Mousa AbdurRahman Ossama Younis Mostafa Saleh Abdelsalam Yara Hisham Tarek Talaat Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha Egypt

Employee turnover and customer churn a significant challenges for organizations worldwide. The loss of employees can significantly impact the company's growth and success, from decreased productivity to increased costs, including recruiting and training new employees. To mitigate this problem, many organizations are adopting artificial intelligence (AI) techniques to detect employee churn. In this approach, AI algorithms like KNN, Decision Tree, Logistic Regression, Random Forest, SVM (Support Vector Machines), ADA boost, Naïve Bayes, and GBM(Gradient Boosted Machine Tree) are trained on historical data. The data set was divided into two segments, with 80% allocated for training and the rest 20% for testing purposes to identify patterns and predict which employees are likely to leave the organization. By doing so, companies can take proactive measures to retain their employees and reduce the costs of hiring and training new ones. Using AI to detect employee churn can potentially provide significant benefits for companies, including improved retention rates, increased productivity, and reduced costs. Overall, using AI to detect employee churn is a promising solution that can help companies retain their employees and achieve their business goals.

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Research on Decentralized Federated Learning System for Vehicle data Privacy Protection Based on Blockchain

Research on Decentralized Federated Learning System for Vehi...

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Image processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Qiong Li Bin Gong Yizhao Zhu Rongsheng Cai Xiangqian Kong Faculty of Data Science City University of Macau Macau China Science and Technology Department Hunan Industry Polytechnic Changsha Hunan China Guangdong Polytechnic of Science and Technology Zhuhai Guangdong China Quanzhou College of Technology Quanzhou Fujian China

This paper proposes an edge computing vehicle data privacy protection method based on blockchain and federated learning. Using blockchain to give edge computing tamper-proof and anti-single point of failure attacks and other characteristics. Integrate gradient verification and incentive mechanism into consensus protocol. By constructing master-slave chain architecture among vehicles, roadside units and base stations, distributed model security sharing is realized. An asynchronous federated learning algorithm based on incentive mechanism is proposed to motivate vehicles and roadside units to participate in the optimization process. The framework is verified on real locus data sets. LSTM self-coding is used as embedded learning network. The prototype of the proposed architecture is implemented in the experiment, and its feasibility, accuracy and performance are evaluated. The results show that this method maintains the integrity of data and has good prediction accuracy and performance.

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Automobile Price Prediction using Machine Learning with data Visualization

Automobile Price Prediction using Machine Learning with Data...

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international conference on computing and Networking Technology (ICCNT)

作者： Nishitha U Revanth Kandimalla Mourya Vardhan Reddy M Jyotsna C Dept. of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

The automobile industry is experiencing a rapid increase in demand due to the growing population, leading to an increase in the sales of automobiles. Industry experts focus on renewing automobiles and increasing their efficiency and power while making them user-friendly and cost-effective. To assist people in purchasing cars within their budget, machine learning algorithms are utilized to predict the price of cars accurately. A machine learning model with high accuracy is integrated into a user-friendly interface, allowing people to view the precise price of the car. The user interface is designed to be user-friendly, making it easy for people to use. This paper presents various regression and classification models and their accuracies and promises to provide the price of the cars according to given inputs with a user-friendly interface. A supervised regression model is developed using multiple regression techniques trained from the car dataset provided. data cleaning is performed to enhance the performance of these regression models by converting null values to non null values in the dataset.

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IoT integration with Big data and Cloud computing: A Multidisciplinary Approach

IoT integration with Big Data and Cloud Computing: A Multidi...

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Communication, Security and Artificial Intelligence (ICCSAI), international conference on

作者： Atul Sharma Kapil Kumar Aggarwal Meena Malik University Institute of Computing Chandigarh University Mohali Punjab India University School of Business Chandigarh University Mohali Punjab India Computer Science & Engineering Chandigarh University Mohali Punjab India

IoT (Internet of Things) has become the most popular technology nowadays which opens chances for home machines, wearable gadgets, and software to share and convey data on the Internet. Internet of things globally connected devices and gained popularity in the telecommunication industry. It has many applications in the area of healthcare, marketing, production, social application and smart home applications etc. But this technology also has some issues related to its implementation in various areas such as data processing and storage, security etc. In this paper, we start with general information of IoT, architecture, applications, and limitations and then the importance of the convergence of IoT, Cloud computing and Big data. Finally, this paper summarizes the researchers about how to leverage the benefits by combining these three technologies.

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Enhancing Security and Privacy in Cloud – Based Healthcare data Through Machine Learning

Enhancing Security and Privacy in Cloud – Based Healthcare ...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), international conference on

作者： Aasheesh Shukla Hemant Singh Pokhariya Jacob Michaelson Arun Pratap Srivastava Laxmi Narayanamma Amit Srivastava Department of Electronics and Communication GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Lloyd Institute of Engineering and Technology Greater Noida Department of Information Technology Institute of Aeronautical Engineering Hyderabad Telangana Lloyd Law College Greater Noida

It is becoming more and more important for healthcare providers to protect the integrity and security of sensitive medical data as they use cloud computing for data processing and storage. This work explores the field of machine learning algorithms that are secure and privacy-preserving when applied to healthcare information in cloud environments. We investigate sophisticated cryptography, federated learning, and differentiating privacy techniques using an interpretive philosophy and a method based on deduction. Our results highlight the computational expense associated with cryptographic protocols, while also revealing their nuanced performance and potential for enabling secure calculations. Federated learning is shown to be effective in collaborative model training, providing a workable approach to privacy-preserving data analysis over-dispersed healthcare datasets. Differential privacy systems require careful parameter calibration because they demonstrate a delicate balance between data value and privacy preservation.

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Deep Learning Based Object Detection and Tracking Method for Flatworm Behavior Analysis

Deep Learning Based Object Detection and Tracking Method for...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Zhao, Yanguo Chen, Leon Wong, Tim Xu, Song Guangzhou Basis International School Guangzhou China Young Scientist Association INC. Shenzhen China

ISBN: (纸本)9798218142186

In marine ecosystems, flatworm species feed on economically important species such as oysters and could increase the mortality and thereby lower the yield. Understanding the behavior of flatworms in response to environmental factors and, more importantly, knowing the distribution patterns of the flatworms in the field is crucial and has important implications for fishery management. Currently, the dominant methods for flatworm observation are all Kalman Filter-based. However, the traditional Kalman Filter-based tracking algorithms were mostly based only on one single frame as the reference template for the tracking of the target while flatworms are highly deformable targets that can have a great degree of shape-shifting. Here, we proposed a workflow of flatworm tracking using deep learning module (YOLOV5 and StrongSort) to track flatworm movement with high accuracy. We conducted a series of food choice essay experiments and verified our workflow by tracking movement and reporting the trajectory of the subject flatworm species. We use data refinement algorithm based on the similarity of images to greatly reduce the dataset size to allow for faster training speed. We used the workflow developed in this study to track the flatworm specimen in different experiment videos and achieved a 100% successful tracking rate (STR) in the tracking of the target. In addition, the tracking method was deployed on edge computing device Nvidia Jetson Nano with the achievement of 7 frames per second, indicating the capability of our algorithm in real-time tracking. More importantly, our workflow successfully detected and then tracked the movement of target flatworm specimen in all experiment videos. Based on the tracking data, we computed the trajectory and the moving speed of the flatworm. It is expected that this algorithm could be further applied to field video data for the tracking of flatworm, particularly the tracking of S. eliptica, and thereby assisting the study of benthic ecol

关键词： Object detection

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American Zebra Optimization Algorithm-based Clustering Protocol for Reliable data Routing in Internet of Things 11th

American Zebra Optimization Algorithm-based Clustering Proto...

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11th international conference on Intelligent computing and Applications, ICRTC 2023

作者： Janakiraman, Sengathir Deva Priya, M. Karthick, S. Department of Information Technology CVR College of Engineering Mangalpally Vastunagar Telangana Hyderabad India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Delhi—NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819717231

In Internet of Things (IoT), devices connect and function in wireless mode for gathering information from the sorrounding environment and performing reactive decision-making. But network lifetime of these IoT devices completely depends on how limited energy resources are optimized during normal functioning such that impactful performance measures get improved eventually. Though quantifiable amount of research works using Swarm Intelligent (SI) algorithms are contributed to the literature for handling and achieving energy efficiency of IoT nodes deployed in the network, there is still a scope of improvement in optimization and routing mechanisms. In this paper, American Zebra Optimization Algorithm-based Clustering Protocol (AZOACP) is proposed for attaining energy efficiency of sensor nodes such that longer network lifetime with reliable data routing is guaranteed in IoT. The adopted American Zebra Optimization Algorithm (AZOA) possesses maximized capability of exploration with rapid rate of convergence such that better Cluster Heads (CHs) are selected for achieving better energy efficiency during routing. It further adopts the factors of initial energy level of nodes, energy availability, distance between CHs and distance between each sensor node and sink, and number of neighbours for selecting CHs in the network. The simulation results of the proposed AZOACP confirm better stability period, network lifetime, throughput and number of alive nodes compared to baseline approaches considered for comparison. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Automation of EEG data processing with HPC Community Cloud

Automation of EEG Data Processing with HPC Community Cloud

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Maxim A. Gorodnichev Elizaveta D. Nalepova Ekaterina A. Merkulova Pavel D. Rudych Alexander N. Savostyanov Scientific Research Institute of Neurosciences and Medicine ICM&MG SB RAS Novosibirsk State University Novosibirsk State Technical University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Scientific Research Institute of Neurosciences and Medicine Novosibirsk State University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia

We study and further develop an approach based on the computational models formalism to automation of data processing pipelines in a specific application domain-neurophysiology. A computational model is a formal specification of a set of variables (in a mathematical sense) of an application domain and a set of operations with their input and output variables. If values of some variables are given, the operations that have these variables as inputs can compute values of their output variables, these variables serve as inputs for other operations and, thus, some subset of variables can obtain their values. This way, all possible/meaningful computational scenarios in the domain can be defined and algorithms (scenarios) for particular computational problems can be automatically derived by forward chaining. In this work, we apply the approach to building a framework for managing data and computations in neurophysiology. Brain studies produce large amounts of data such as electroencephalograms, functional magnetic tomography images, questionnaires, and results of other medical tests. There is a need for systematic management of these data, specification of data processing pipelines, implementing these pipelines on high performance computing systems, representing the process of computing and the results with a graphic user interface. We build a sample computational model to represent a limited set of scenarios that can be reused and further extended in a regular way by adding more variables and operations, propose a framework for management of data and computations, automation of building graphical user interfaces. A prototype implementation of the framework based on the developed computation model is a practical output of this work.

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data Augmentation Method on Drone Object Detection with YOLOv5 Algorithm

Data Augmentation Method on Drone Object Detection with YOLO...

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international conference on Informatics and computing (ICIC)

作者： Ariel Yonatan Alin Kusrini Kumara Ari Yuana Magister of Informatics Engineering Universitas AMIKOM Yogyakarta Yogyakarta Indonesia

This study aims to evaluate and compare various types of data augmentation in drone object detection using the YOLOv5 algorithm. data augmentation is an important technique in image data processing to increase the diversity and quality of training data, so as to improve the performance of object detection models. In this study, several different types of data augmentation were applied to the existing drone object detection dataset. This research method consists of fourteen types of data augmentation, namely Flip, Rotate, Crop, Rotation, Shear, Hue, Blur, Grayscale, Saturation, Brightness, Exposure, Noise, Cutout, and Mosaic. The use of the Mosaic method as the newest type of data augmentation is expected to provide better results in drone object detection. The test is carried out by measuring the Precision-Recall evaluation metrics for each type of data augmentation. The results showed that the Mosaic augmentation type gave the highest Precision-Recall value of 0.993 compared to other augmentation types. The use of Mosaic as a data augmentation in drone object detection with the YOLOv5 algorithm can significantly improve object detection performance.

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