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Brain Stroke Prediction Using Deep Learning: A CNN Approach 4

Brain Stroke Prediction Using Deep Learning: A CNN Approach

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Reddy, Madhavi K. Kovuri, Karthik Avanija, J. Sakthivel, M. Kaleru, Shivaprasad Sree Vidyanikethan Engineering College Department of Cse Andhra Pradesh Tirupati India Cse B v Raju Institute of Technology Telangana Narsapur India Juniper Networks Inc Software Engineer Sr Staff SunnyvaleCA United States

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. It's a medical emergency;therefore getting help as soon as possible is critical. Seeking medical help right away can help prevent brain damage and other complications. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome, and supporting doctors in prescribing disease treatment. Using a deep learning model on a brain disease dataset, this method of predicting analytical techniques for stroke was carried out. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. In addition, three models for predicting the outcomes have been developed. this suggested study uses a CT scan (computed tomography) image dataset to predict and classify strokes. © 2022 IEEE.

关键词： Forecasting

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Machine Learning for Predicting Fatigue Life of Concrete Under Cyclic Compression: A Comparative Evaluation of Regression models

Machine Learning for Predicting Fatigue Life of Concrete Und...

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6th international conference on Advanced Materials for Photonics, Sensing, and Energy Applications, AMPSECA 2024

作者： Moussaddaq, Chouaib Yahya, Riyad Madiha, Yessari Laboratory of Engineering Sciences for Energy University of Chouaib Doukali El Jadida Morocco

ISBN: (纸本)9789819663774

the fatigue life of concrete under cyclic compression is a critical factor in assessing the durability and safety of concrete structures. In this study, we utilize machine learning techniques to predict the fatigue life, based on key material and stress parameters. the dataset, comprising samples of concrete tested under cyclic loading conditions, includes variables such as compressive strength (Fc), minimum and maximum cyclic stresses (Smin, Smax). We apply several regression models, including Linear Regression, Random Forest Regressor, and Gradient Boosting Regressor, to predict the fatigue life. Each model's performance is evaluated using R-squared (R2) metric. Preliminary findings indicate that ensemble methods, particularly Random Forest and Gradient Boosting, show superior predictive performance compared to traditional linear regression. these results underscore the potential of machine learning models to enhance fatigue life prediction. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Linear regression

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Some Investigations of Machine Learning models for software Defects

Some Investigations of Machine Learning Models for Software ...

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international conference on software engineering - Companion, ICSE, Companion

作者： Umamaheswara Sharma Bhutamapuram Department of Computer Science and Engineering National Institute of Technology Warangal Warangal India

software defect prediction (SDP) and software defect severity prediction (SDSP) models alleviate the burden on the testers by providing the automatic assessment of a newly-developed program in a short amount of time. the research on defect prediction or defect severity prediction is primarily focused on proposing classification frameworks or addressing challenges in developing prediction models; however, the primary yet significant gap in the literature is interpreting the predictions in terms of project objectives. Furthermore, the literature indicates that these models have poor predictive performance. In this thesis, we investigate the use of a diversity-based ensemble learning mechanism for the cross-project defect prediction (CPDP) task and self-training semi-supervised learning for the software defect severity prediction, respectively, for obtaining better prediction performances. We also propose a few project-specific performance measures to interpret the predictions in terms of project objectives (such as a reduction in expenditure, time, and failure chances). through the empirical analysis, we observe that (1) the diversity-based ensemble learning mechanism improves the prediction performance in terms of both the traditional and proposed measures, and (2) the self-training semi-supervised learning model has a positive impact on predicting the severity of a defective module. Once a potential prediction model is developed, any software organisation may utilise its services. How can an organisation showcase their trust in the developed prediction model? To this end, we investigate the feasibility of SDP models in real-world testing environments by providing proofs using the probabilistic bounds. the proofs summarised show that even if the prediction model has a lower failure probability, the probability of obtaining fewer failures in SDP-tested software than in similar but manually tested software is still exponentially small. this result enables the researche

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Simulating Product Costs Under Stochastic Conditions Using the ABC Matrix Model 1

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17th international conference on Interdisciplinarity in engineering, INTER-ENG 2023

作者： Henriques, Marcelo F. N. Dias, Luís Silva Afonso, Paulo Ferreira, Luís Pinto Department of Production and Systems School of Engineering University of Minho Campus of Azurém Guimarães4804-533 Portugal ALGORITMI Research Centre/LASI University of Minho Campus of Azurém Guimarães4804-533 Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto4249-015 Portugal Rua Dr. Roberto Frias 400 Porto4200-465 Portugal

ISBN: (数字)9783031546716

ISBN: (纸本)9783031546709

Costing models tend to follow a top-down or, alternatively, a bottom-up approach, following, respectively, a financial/accounting or an engineering/production perspective. Regardless of the approach used, cost models tend to be based on past information and be deterministic, contributing little to decision making at the organization’s operational and strategic levels. Moreover, the current business environment is characterised by conditions of high variability, uncertainty, complexity, and ambiguity. As such, predictive models for decision making which consider the stochastic nature of certain variables are beneficial. In this paper, we present a matrix activity-based costing model which allows a combined bottom-up and top-down approach to predict the resource requirements of a production system and to compute the cost of products and other relevant cost objects, considering the processes as deterministic or stochastic. the developed costing model includes the main elements which characterise a production system. therefore, it can be easily generalized to different processes and industries. this proposed costing model was replicated using the SIMIO simulation software, including parameters to both make the test process efficient and allow to take full advantage of experimentation. Moreover, tallies were modelled to exhibit custom results, suited to this matrix costing model. We have concluded that discrete event simulation is a well suited tool to cost modelling, especially given its stochastic capabilities and the ease of scenario creation. In future work, different system aspects can be assessed, different resource combination, and the cost per resource call;can be estimated and the simulation model can be improved to facilitate scenario creation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Discrete event simulation

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Machine Learning-Based Multi-stratum Channel Coordinator for Resilient Internet of Space things 6th

Machine Learning-Based Multi-stratum Channel Coordinator for...

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6th IFIP international conference on Internet of things, IFIP IoT 2023

作者： Islam, Md Tajul Song, Sejun Choi, Baek-Young School of Science and Engineering University of Missouri-Kansas City Kansas CityMO United States

ISBN: (纸本)9783031458774

Sensing and transferring data are critical and challenging tasks for space missions, especially in the presence of extreme environments. Unlike terrestrial environments, space poses unprecedented reliability challenges to wireless communication channels due to electromagnetic interference and radiation. the determination of a dependable channel for exchanging critical data in a highly intemperate environment is crucial for the success of space missions. this paper proposes a unique Machine Learning (ML)-based multi-stratum channel coordinator in building the Resilient Internet of Space things (ResIST). ResIST channel coordinator accommodates a lightweight software-defined wireless communication topology that allows dynamic selection of the most trustworthy channel(s) from a set of disparate frequency channels by utilizing ML technologies. We build a tool that simulates the space communication channel environments and then evaluate several prediction models to predict the bandwidths across a set of channels that experience the influence of radiation and interference. the experimental results show that ML-prediction technologies can be used efficiently for the determination of reliable channel(s) in extreme environments. Our observations from the heatmap and error analysis on the various ML-based methods show that Feed-Forward Neural Network (FFNN) drastically outperforms other ML methods as well as the simple prediction baseline method. © 2024, IFIP international Federation for Information Processing.

关键词： Forecasting

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Data vs. Model Machine Learning Fairness Testing: An Empirical Study

Data vs. Model Machine Learning Fairness Testing: An Empiric...

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international conference on software engineering - Companion, ICSE, Companion

作者： Arumoy Shome Luís Cruz Arie van Deursen Delft University of Technology Delft Netherlands

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Although several fairness definitions and bias mitigation techniques exist in the literature, all existing solutions evaluate fairness of Machine Learning (ML) systems after the training stage. In this paper, we take the first steps towards evaluating a more holistic approach by testing for fairness both before and after model training. We evaluate the effectiveness of the proposed approach and position it within the ML development lifecycle, using an empirical analysis of the relationship between model dependent and independent fairness metrics. the study uses 2 fairness metrics, 4 ML algorithms, 5 real-world datasets and 1600 fairness evaluation cycles. We find a linear relationship between data and model fairness metrics when the distribution and the size of the training data changes. Our results indicate that testing for fairness prior to training can be a “cheap” and effective means of catching a biased data collection process early; detecting data drifts in production systems and minimising execution of full training cycles thus reducing development time and costs.

关键词： Training Measurement Analytical models Training data Machine learning predictive models Data models

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the Role of Sentiment Analysis and Transformer models in Stock Market Price Forecasting

The Role of Sentiment Analysis and Transformer Models in Sto...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Durga Meena R Maharajan K Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Stock market analysis is one of the most important, to understand the dynamics of stock movements. this includes utilizing the available data and analyzing past time series data for making the decisions. the forecasting stock prices is a daunting task due to various number of diverse factors. In order to apply sentiment analysis techniques, these factors should have considered carefully for more accurate predictions. Now-a-days there are more number of prediction methodologies which may be used independently or as a hybrid approach. Amid these machine learning techniques have been revealed good performances in the prediction process. the objective of this research is to examine the utilization of transformer models in order to perform sentiment analysis and prediction of stock market. this review highlights the current state in the field of stock market prediction in the discovery of the research gaps, the suitable methodologies, and challenges, along with future directions. this paper is useful to researchers and practitioners in this direction towards more robust and accurate predictions of stock markets.

关键词： Sentiment analysis Analytical models Accuracy Computational modeling Time series analysis predictive models Transformers Indexes Stock markets Forecasting

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Enhancing Deep Learning Performance through Parallel Processing: A Comprehensive Research Study

Enhancing Deep Learning Performance Through Parallel Process...

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international conference on Recent Trends in Advance Computing (ICRTAC)

作者： Rudraditya thakur Naman Goel Bhavesh Vaidehi Vijaykumar School of Computer Science Engineering Vellore Institute Of Technology Chennai Chennai India

the computational demands of training complex deep learning models have hindered the widespread adoption of this transformative technology. Parallel processing techniques have emerged as a promising approach to address this challenge by distributing the computational workload across multiple processors. this research delves into the multifaceted dimensions of enhancing deep learning performance through parallel processing. It explores the theoretical underpinnings, parallelization strategies, hardware and software infrastructures, and application-specific impact of parallel processing in deep learning.

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Detection of Periapical Lesions in Dental X-Rays and Oral Scan Using Computer Vision Model

Detection of Periapical Lesions in Dental X-Rays and Oral Sc...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： N. C. Brintha D. Gurukiran N. Deekshith D. Mahidhar Dayina Sanjay Dept of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Periapical lesions are abnormal growths or infections that develop close to the tip of a tooth's root and can be caused by oral trauma, decaying teeth, or dental work done in the past. It is particularly difficult to accurately identify and differentiate periapical lesions, which presents a major diagnostic problem. Density measurements are not reliable and can't be linked to histology. Yolov5 which detect objects in real time with great accuracy is used to automatically identify and categorize lesions from imaging data. this model is a good object detector to detect small objects. It is the fastest model compared to other models.

关键词： YOLO Training Accuracy Computational modeling Teeth X-rays predictive models Dentistry Lesions Reliability

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Spatiotemporal-Aware Heterogeneous Graph Attention Network for Information Diffusion Prediction

Spatiotemporal-Aware Heterogeneous Graph Attention Network f...

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international conference on Electronic engineering and Informatics (EEI)

作者： Zixin Xu School of Computer Science & Information Engineering Shanghai Institute of Technology University Shanghai China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Information diffusion is the process by which information spreads through a network of individuals or nodes, which is crucial for understanding and predicting social behavior. Research indicates that the diffusion of information is shaped by the network's structure, the personal characteristics of users, and the nature of the information itself. However, most models ignore the social network graph and treat the diffusion path of information as a sequence rather than a graph. this paper proposes a novel Spatiotemporal-Aware Heterogeneous Graph Attention Network (SAHGAT). the model constructs a heterogeneous graph that integrates user social attention and reposting behavior, using Graph Attention Network (GAT) to learn a comprehensive representation of users in terms of social relationships and information dissemination behavior. It combines users' social features, location, and time information to generate representations of the cascade sequence, aiming to predict the next user likely to participate in reposting. Experimental results on three datasets, including Twitter, show that this model outperforms models that ignore the social graph and treat the diffusion path only as a sequence.

关键词： Neural networks Blogs predictive models Parallel processing Spatiotemporal phenomena Information diffusion Informatics

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