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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是441-450 订阅

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A Covid-19 Epidemiological Analysis and Forecasting Dashboard for Hospitals using Time-Series Analysis and Deep learning 3

A Covid-19 Epidemiological Analysis and Forecasting Dashboar...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Famadico, Nichol John F. Solano, Geoffrey A. Caoili, Janice C. Univ Philippines Coll Arts & Sci Dept Phys Sci & Math Manila Philippines Makati Med Ctr Infect Prevent & Control Dept Makati Philippines Trop Dis Fdn Makati Philippines

ISBN: (纸本)9798350372977;9798350372984

COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that, to date, has over 774 million confirmed cases and claimed over 7 million lives. Forecasts are therefore essential as they aide in the necessary preparation of healthcare logistics. In this study, forecasting models such as ARIMA, RNN and LSTM were developed to predict Covid-19 time-series data relevant to hospitals, which were then evaluated and compared using MAE and RMSE. Across the different variables, LSTM had the least error. A web-based dashboard was also developed for forecasting these time-series variables for healthcare institutions, particularly those which are accommodating Covid-19 patients.

关键词： Covid-19 dashboard time series forecasting ARIMA RNN LSTM

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SGBCD: A data Discretization Algorithm Based on Granular-ball Computing and Attribute Significance 3

SGBCD: A Data Discretization Algorithm Based on Granular-bal...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Chen, Weiqi Chongqing Vocational College of Transportation Chongqing China

ISBN: (纸本)9798350380989

As an important method in data preprocessing, discretization can effectively reduce the size of data, generate concise semantic representation, obtain valuable knowledge and information contained in big data, which is of great significance in the fields of data mining and machine learning. Nevertheless, most of the traditional discretization algorithms don't consider the distribution of sample and difficulty in setting parameters during interval partitioning, which leads to a decrease in efficiency and classification accuracy. A data discretization algorithm based on granular-ball computing and attribute significance is proposed in this paper, which is a multi-granularity method. Firstly, by introducing the granular-ball computing, the difficulty of parameter optimization is reduced and the efficiency of the algorithm is improved. At the same time, interval partitioning adaptively fits the original data distribution, reducing the occurrence of uneven interval partitioning and improving classification accuracy. Then, the randomness of attribute selection leads to instability in interval partitioning results. By introducing attribute significance and prioritizing the selection of attributes with high importance, this paper further improves the classification accuracy. Compared to other excellent discretization algorithms in the experiments, the proposed algorithm shows an ideal performance. © 2024 IEEE.

关键词： attribute significance discretization granular-ball computing interval partitioning multi-granularity

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Detection of Position Falsification Attacks in VANETs Using Ensemble learning 3

Detection of Position Falsification Attacks in VANETs Using ...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Roopa Devi, E.M. Shanthakumari, R. Harini, T. Lokesh, K. Anusuyaa, V.S. Kongu Engineering College Department of Information Technology Erode India

ISBN: (纸本)9798350372120

Vehicular Ad Hoc Networks (VANETs) are utilised by Cooperative Intelligent Transport Systems (C-ITS) to facilitate smooth communication between vehicles and infrastructure, markinga significant technological advancement in traffic efficiency and road safety. However, the security of VANETs is a paramount concern in C-ITS, as potential attacks could compromise passenger safety. Intrusion Detection Systems (IDS) are pivotal in safeguarding vehicular networks by identifying misbehaving vehicles. A misbehaving network could interfere with any data in the propagated packet. Fraudulent messages can be made by the attacker node or by forcing another network node to create fraudulent messages. It introduces a novel approach to enhance IDS performance in the context of position falsification attacks. machine learning uses algorithms to extract patterns from data, whereas ensemble learning mixes many models to increase prediction accuracy. Ensemble models outperform individual machine learning algorithms in detecting the position falsification attacks because they lower noise, incorporate various inputs, reduce bias and variance, detect abnormalities effectively, and capture complex data relationships. The performance of four distinct ML methods for classification was compared that is KNN, Random Forest, Gradient Boosting and XGBoost. Finally, the XGBoost, short for Extreme Gradient Boosting out performs with high accuracy of 97% with efficient and effectiveness which is ensemble learning technique that combine the predictions of multiple weak learners. The results demonstrate that, on average, the proposed mechanism performs better than earlier approaches as a result of computational time and performance on classification criteria. © 2024 IEEE.

关键词： learning systems

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Detection and Classification of Brain Tumor Using machine learning 3

Detection and Classification of Brain Tumor Using Machine Le...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Kale, Prachi V. Gadicha, Ajay B. Dalvi, G.D. P R Pote College of Engineering & Management Department of Computer Science and Engineering Maharashtra Amravati India P R Pote College of Engineering & Management Department of AI Maharashtra Amravati India Allard College Department of EXTC Maharashtra Pune India

ISBN: (纸本)9798350391565

Diagnosing a brain tumor typically necessitates the expertise of a radiologist, whose skills and knowledge are essential to the process. Since the overall number of individuals has grown, so too has the quantity of medical data required to be managed, rendering outdated methods extremely costly and inefficient. Many investigators looked into different techniques that had been quick and effective in identifying and categorizing brain tumours. Currently, machine learning (ML) techniques have gained popularity in the development of computerized systems that can quickly and effectively diagnose or segment brain tumours. The deep learning (DL) enables the application of ML frameworks for brain tumor identification. This paper proposed various ML models to analyze the MRI images. First, preprocessing and augmentation algorithms were applied to the MRI brain images. We evaluated the performance of several models, including LR, SVC, kNN, NB, NN, RF, and K-means clustering. The analysis focused on accuracy, precision, recall, F1-score, and AUC. LR (96%), NN (95%), and RF (96%) showed the highest performance across all metrics, effectively distinguishing between tumor and no-tumor images and providing reliable results. © 2024 IEEE.

关键词： K-means clustering

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2024 3rd international conference on Artificial Intelligence for Internet of Things, AIIoT 2024

2024 3rd International Conference on Artificial Intelligence...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

ISBN: (纸本)9798350372120

The proceedings contain 266 papers. The topics discussed include: prediction of Parkinson’s disease with various ML and DL techniques on speech data;iterative imputation of incomplete wireless network traffic using adversarial learning and a two-stage approach;photoplethysmogram (PPG)-based blood pressure estimation using vision transformer networks: a deep learning approach;error identification in translation look-aside buffer;exploring Jukebox: a novel audio representation for music genre identification in MIR;computer vision based algorithms for detecting and classification of activities for fall recognition on real time video;advancements in neurological health: predicting brain age with machine learning;planar u-shaped slot multiresonator-based chipless RFID tags;and design and analysis of helical antenna for GPS systems.

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Detecting anomalies in forest input data in a supply chain system using machine learning 3

Detecting anomalies in forest input data in a supply chain s...

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3rd international conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

作者： Vasendina, I.S. Shoshina, K.V. Aleshko, R.A. Berezovsky, V.V. Federal University named after M.V. Lomonosov 17 Northern Dvina Embankment Arkhangelsk Oblast Arkhangelsk163002 Russia

ISBN: (纸本)9781510681590

The study is devoted to the problem of verifying the entry of correct data into a distributed registry system for accounting and monitoring of forest resources based on blockchain technology. The features and significance of preprocessing data entered into the system for the presence of anomalies are considered. A method for identifying anomalies in data entered into such a system is proposed using the example of processing data on wood reserves in the territory. The technique is based on mixing statistical approaches and machine learning models. The technique was tested on data from automated thematic interpretation of images. Methods for scaling the methodology to identify data anomalies throughout the entire cycle of the forestry industry are described. © 2024 SPIE.

关键词： Anomaly detection

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Identifying architectural design decisions for achieving green ML serving 24

Identifying architectural design decisions for achieving gre...

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Duran, Francisco Martinez-Fernandez, Silverio Martinez, Matias Lago, Patricia Univ Politecn Cataluna Barcelona Spain Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400705915

The growing use of large machine learning models highlights concerns about their increasing computational demands. While the energy consumption of their training phase has received attention, fewer works have considered the inference phase. For ML inference, the binding of ML models to the ML system for user access, known as ML serving, is a critical yet understudied step for achieving efficiency in ML applications. We examine the literature in ML architectural design decisions and Green AI, with a special focus on ML serving. The aim is to analyze ML serving architectural design decisions for the purpose of understanding and identifying them with respect to quality characteristics from the point of view of researchers and practitioners in the context of ML serving literature. Our results (i) identify ML serving architectural design decisions along with their corresponding components and associated technological stack, and (ii) provide an overview of the quality characteristics studied in the literature, including energy efficiency. This preliminary study is the first step in our goal to achieve green ML serving. Our analysis may aid ML researchers and practitioners in making green-aware architecture design decisions when serving their models.

关键词： AI-based systems machine learning Architectural Design Decisions green AI

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Enhancing Heart Murmur Detection: A Comparative Study of machine learning Models Utilizing Digital Stethoscopes 3

Enhancing Heart Murmur Detection: A Comparative Study of Mac...

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3rd IEEE World conference on Applied Intelligence and Computing, AIC 2024

作者： Mastracci, Nathaniel Derakhshan, Farnaz Sykes, Edward R. Abdullah, Saad Oakville Canada University of Guelph School of Computer Science Guelph Canada Mälardalen University School of Innovation Design and Engineering Västerås Sweden

ISBN: (纸本)9798350384598

machine learning has proven to be a powerful tool across many domains including healthcare. Heart sound classification using machine learning can revolutionize cardiac care by improving diagnostic accuracy, enabling early intervention, and facilitating personalized treatment strategies. However, obtaining labeled data for classification model training can be difficult, especially for rare or complex conditions. Furthermore, classifying heart sounds accurately can be challenging due to the variability in sound patterns and the presence of noise which requires preprocessing. In this paper, two machine learning models were trained and evaluated using DenseNet architecture on the CirCor DigiScope Phonocardiagram dataset and an ensemble of Support Vector machine (SVM) and Decision Tree (DT) algorithms on a custom dataset curated by our partner, Tech4Life. The F1 score of the CirCor trained model was 75%. This is our effort to advance the application of machine learning in heart sound classification. © 2024 IEEE.

关键词： Contrastive learning

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Navigating data Abundance: Generative Conversational AI Agents in Information Analysis 3

Navigating Data Abundance: Generative Conversational AI Agen...

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sahu, Barnali Das, Sanjog Pattnaik, Ayush Puri, Abhishek Mohanty, Kasmik Computer Science & Engineering Department Bhubaneswar India

ISBN: (纸本)9798350354379

In response to the escalating challenges posed by information overload, particularly in today's data-rich environment, we propose the development of a sophisticated Generative Conversational AI Agent (GCAIA) tailored specifically for information retrieval tasks. This innovative system integrates cutting-edge techniques in natural language processing, machine learning, and generative modeling to provide users with an intuitive platform for accessing, analyzing, and retrieving information across diverse domains. Key features include robust natural language understanding facilitating seamless interaction, efficient retrieval mechanisms enabling quick access to relevant data, contextual adaptation for delivering personalized responses, generative capabilities fostering natural and engaging conversations, multimodal integration supporting various data formats, and utilization of knowledge graphs for enhanced organization and semantic understanding. Through rigorous research, development, and evaluation processes, our goal is to demonstrate the effectiveness, reliability, and scalability of the GCAIA in significantly improving information retrieval processes across diverse domains. Ultimately, our aim is to empower users to navigate the ever-expanding landscape of knowledge and data effectively, thus mitigating the challenges posed by information overload in the digital age. © 2024 IEEE.

关键词： Generative adversarial networks

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Performance Improvements of machine learning-Based Crime Prediction, A Case Study in Bangladesh 3

Performance Improvements of Machine Learning-Based Crime Pre...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Nobel, S. M. Nuruzzaman Swapno, S. M. Masfequier Rahman Islam, Md Babul Meena, V. P. Benedetto, Francesco Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka Bangladesh UNICAL Dept Comp Modeling Elect & Syst Engn Arcavacata Di Rende Italy Amrita Vishwa Vidyapeetham Dept EEE Amrita Sch Engn Bengaluru Coimbatore India Univ ROMA TRE Dept Econ SP4TE Lab Via Silvio Rome Italy

ISBN: (纸本)9798350372977;9798350372984

Crime observation examines information on criminal episodes to identify trends in their types, locations, and timing. Forecasting probable criminal activity in some places exploits previous data and data analysis to predict future criminal activity. Crime prediction faces several challenges, and accuracy can be elusive, mainly when dealing with diverse locations. Here, we propose a machine learning-based method, namely the extra tree regressor model, for crime prediction, with specific emphasis on different crime categories like dacoity, robbery, and murder, as well as geographical areas, including metropolitan regions and divisions. We decided to focus our analysis on a particular case study, i.e., crime data about Bangladesh, thus exploiting the crime data taken from the Bangladesh police department website. Our results and comparisons with state-of-the-art methods, such as linear regression, randomforestregressor, XGBregressor, and gradientboosting regressor, demonstrate the superiority of the proposed approach. In particular, our method can reach an accuracy rate of 99.95%, thus proving to be very effective in predicting and analyzing Bangladesh's criminal activity and understanding crime patterns and trends.

关键词： Crime prediction Crime Rate Crime Area Accurately Extra Trees Regressor

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