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Detection and Classification of Brain Tumor and 3D Reconstruction of MRI 5

Detection and Classification of Brain Tumor and 3D Reconstru...

引用

5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Kongalla, Maheshwari Desai, Rhutuja Vangepuram, Benson Padma, T. Gokaraju Rangaraju Institute of Engineering and Technology Department of ECE Hyderabad India

ISBN: (纸本)9798350367171

Brain tumors pose a significant global health concern, with millions affected worldwide, often resulting in death. In the United States, approximately 70,000 people have primary brain tumors, making brain cancer the 10th most common tumor in India. Accurate diagnosis is crucial for initiating appropriate treatment, and improving patient survival. Treatment varies based on tumor type, size, location, and aggressiveness. Deep learning methods, like transfer learning-based techniques using traditional classifiers, are increasingly used for tumor detection in MRI images. Our study explores VGG-16 and VGG-19 transfer learning methods, leveraging 3D Slicer's adaptable algorithm for reconstructing MRI scans of brain tumors. By utilizing robust software components like Tcl/Tk, VTK, and ITK, alongside an intuitive application layer, we enable advanced functionalities such as image-guided surgery and brain mapping. Our work highlights 3D Slicer's alignment with research goals, despite challenges like prolonged diagnosis times due to workload pressures, potentially leading to errors. this predictive models enhance patient outcomes in this critical medical domain, with improved ROC-AUC score >0.9 and classification accuracy of ∼99%. © 2024 IEEE.

关键词： Deep learning

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Predicting Student Academic Performance Using an Optimised Regression Machine Learning Model 9

Predicting Student Academic Performance Using an Optimised R...

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9th IEEE international conference on Adaptive Science and Technology, ICAST 2024

作者： Martey, Ezekiel Mensah Abdullah, Abubakar Appiah, Obed Department of Computer Science and Engineering Tarkwa Ghana Department of Computer Science and Informatics Sunyani Ghana

ISBN: (纸本)9798350385403

Machine learning has emerged as one transformative tool in predicting student academic performance. this study evaluates machine learning models regression in predicting student academic performance. It can be found optimised Support Vector Regression (SVR) model accurately predicts student academic performance using a benchmark dataset from Kaggle. the approach incorporated Recursive Feature Elimination (RFE) for optimal feature selection while key parameters of the SVR model are tuned through grid search optimisation to enhance model performance. the optimised SVR model achieves exceptional predictive accuracy with an R-squared value of 0.97, Mean Absolute Error of 2.98, Mean Squared Error of 13.28, Root Mean Squared Error of 3.64, Median Absolute Error of 2.65, and Mean Absolute Percentage Error of 0.23. Comparative analysis demonstrates the superior performance of optimised SVR model against Decision Tree, Random Forest, K-Nearest Neighbors, Bayesian Ridge, and Gradient Boosting models. Experimental results demonstrate the validity of robustness and reliability of our approach, offering valuable insights for educational institutions to develop targeted interventions and support systems. © 2024 IEEE.

关键词： Educational Data Mining Grid Search Optimization Machine Learning Recursive Feature Elimination Student Performance Prediction

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks 9

Identify Initial Nodes for Spreader in A New Diffusion Model...

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9th international conference on Web Research, ICWR 2023

作者： Khosravi, Ehsan Yazdanifar, Fatemeh Salimi, Nikoo Beni, Hamid Ahmadi Sharif University of Technology Dept. of Computer Engineering Tehran Iran Amirkabir University of Technology Dept. of Mathematics and Computer Science Tehran Iran Engineering and Naval Architecture University of Genoa Dept. of the Electrical Electronics Electronics and Telecommunication Genoa Italy Azarbaijan Shahid Madani University Dept. of Software Engineering Tabriz Iran

ISBN: (纸本)9798350399691

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. the influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. this problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time. © 2023 IEEE.

关键词： Topology

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LOGI: An Empirical Model of Heat-Induced Disk Drive Data Loss and Its Implications for Data Recovery 18

LOGI: An Empirical Model of Heat-Induced Disk Drive Data Los...

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18th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Ahmad, Hammad Holoday, Colton Bertram, Ian Angstadt, Kevin Sharafi, Zohreh Weimer, Westley Univ Michigan Ann Arbor MI 48109 USA MathWorks Natick MA USA St Lawrence Univ Canton NY USA Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9781450398602

Disk storage continues to be an important medium for data recording in software engineering, and recovering data from a failed storage disk can be expensive and time-consuming. Unfortunately, while physical damage instances are well documented, existing studies of data loss are limited, often only predicting times between failures. We present an empirical measurement of patterns of heat damage on indicative, low-cost commodity hard drives. Because damaged hard drives require many hours to read, we propose an efficient, accurate sampling algorithm. Using our empirical measurements, we develop LOGI, a formal mathematical model that, on average, predicts sector damage with precision, recall, F-measure, and accuracy values of over 0.95. We also present a case study on the usage of LOGI and discuss its implications for file carver software. We hope that this model is used by other researchers to simulate damage and bootstrap further study of disk failures, helping engineers make informed decisions about data storage for software systems.

关键词： n-gram model disk drive damage data recovery software

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software Development for Building Simulation models of Digital Twins of TPP Equipment in Diagnostic Systems 4

Software Development for Building Simulation Models of Digit...

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4th international Youth conference on Radio Electronics, Electrical and Power engineering, REEPE 2022

作者： Belov, Michael Shcherbatov, Ivan Agibalov, Vladimir Dolgushev, Aleksey Ziazev, Sergey National Research University 'MPEI' Department of Automated Control Systems for Thermal Processes Moscow Russia National Research University 'MPEI' Department of Innovative Technologies for High-Tech Industries Moscow Russia National Research University 'MPEF' Department of Computing Machines Systems and Networks Moscow Russia

ISBN: (纸本)9781665414340

this paper deals with the development of mathematical, algorithmic and software for building simulation models of digital twins of thermal power plant (TPP) equipment and thermal circuits for use as part of diagnostic and predictive analytics systems. the database is created, which allows storing thermal and technical parameters of the object and data of the technical state of equipment. An interface for working with digital models of a low-pressure heater, steam cylinder and condenser is developed, which allows creating various schemes of equipment mutual arrangement as a part of the thermal scheme. the functional capabilities of the software are described and the results of its operation are given, as well as the estimations of the technical condition of the equipment at the given parameters. © 2022 IEEE.

关键词： thermoelectric power plants

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A Switch-Transformer predictive Process Monitoring Model for Next Activity Prediction in Business Process Management 24

A Switch-Transformer Predictive Process Monitoring Model for...

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8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Nguyen, thanh-Hai Kim, Kyoung-Sook Kim, Kwanghoon Pio Thai Nguyen Thai Nguyen Viet Nam Contents Convergence Software Research Institute Kyonggi University Gyeonggi Suwon Korea Republic of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon Korea Republic of

ISBN: (纸本)9798400718014

this paper proposes a deep learning model for predicting the next activities in a temporal sequence of activity-events associated with a single process instance. the proposed model is based on a mixture of experts (MoE) mechanism supported by the Switch-Transformer neural network architecture. the deep learning model is trained from scratch on process enactment event log histories. the ultimate goal is to predict the next activities, generate their probabilities of occurrence, and visualize all the predicted next activities in a graphical form of temporal order representing each running case of the corresponding process model. To assess the operational effectiveness of the proposed model, we successfully conducted performance evaluations through a series of experiments on synthetic and real-life process event log datasets, which have been typically and usefully applied in other approaches in previous research works. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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Estimation of SAMG Entry Time in NPPs Using LSTM with Uncertainty Evaluation 9

Estimation of SAMG Entry Time in NPPs Using LSTM with Uncert...

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9th international conference on Frontiers of Signal Processing, ICFSP 2024

作者： Kim, Min Seon Jo, Hye Seon Na, Man Gyun Chosun University Dept. of Nuclear Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350353235

the entry condition of the Severe Accident Management Guideline (SAMG) in Nuclear Power Plants (NPPs) is determined by the Core Exit Temperature (CET). If the CET exceeds 922 K (1200 °F), severe accident management begins. Because a severe accident can induce a large scale of damage, it is necessary to prepare for such accident and take preemptive actions. However, the operators may be confused by the complexity of the system, which can delay actions. therefore, operators need the entry time information of SAMG to act proactively. In this study, the entry time was predicted through CET prediction. the Explainable Boosting Machine (EBM) model was used to select the input variables and the Long Short-Term Memory (LSTM) model was used to predict CET 600 seconds ahead. And the Monte Carlo (MC) dropout method was used to evaluate the uncertainty of the predictions at a 95% confidence level. As a result, the LSTM model performed well and the evaluated uncertainty provided confidence in the predictions with a confidence interval. Predicting 600 seconds ahead provides time for the operators to take actions on the accident, and the uncertainty evaluation adds reliability to the model's prediction. the results of this study are expected to be used as part of the operator support system and serve as a means for rapid accident mitigation actions. Furthermore, the integration of AI-based predictive models and uncertainty evaluations ensures that operators are equipped with reliable information, enhancing their ability to act preemptively and effectively in response to severe accidents. © 2024 IEEE.

关键词： Nuclear power plants

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Approaches for Cryptocurrency Price Prediction 9

Approaches for Cryptocurrency Price Prediction

引用

9th IEEE international conference on Problems of Infocommunications Science and Technology, PIC S and T 2022

作者： Byzkrovnyi, Oleksandr Smelyakov, Kirill Chupryna, Anastasiya Kharkiv National University of Radio Electronics Department of Software Engineering Kharkiv Ukraine

ISBN: (纸本)9798350398915

this paper describes approaches to forecast Ethereum price based on regression analysis which are based on defined in this research list of factors which may affect price. these parameters can be a part of fundamental and technical analysis. In scope of forecasting the nonlinear regression models are used and compared, in couple with prediction of each factor which is used for regression by NeuralProphet. the models' outputs were retrieved during experiment. Also, experiment includes models tuning to have more accurate result. the data time window for experiment is one year. this paper does not consider influence of political situation and nature cataclysms on cryptocurrency. Also, this research does not include index of openness of countries finance institute. the type of analyzed crypto is decentralized finance. the Java microbenchmark harness is used to calculate time which is spent for models training. models' performance is calculated by evaluation of regression metrics: Root mean square error, Mean absolute error, Mean square error, Explained variance. © 2022 IEEE.

关键词： Machine learning

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Addressing Industrial Needs with the Fulib Modeling Library 9th

Addressing Industrial Needs with the Fulib Modeling Library

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9th international conference on Model-Driven engineering and software Development (modelsWARD)

作者： Zuendorf, Albert Kunz, Adrian Eickhoff, Christoph Kassel Univ Kassel Germany

ISBN: (纸本)9789897584879

Fulib is a new lightweight modeling tool providing code generation and model transformations. Code generated by Fulib does not need a Fulib runtime library. Fulib has been designed to be integrated into agile software development processes. Fulib collaborates with versioning tools like Git. these features address practical problems with modeling tools that frequently prevent their usage in industry.

关键词： models Model Transformations Modeling Tools

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A Method for Municipalities to Enhance Disaster Resilience for Potential Future Floods Using a Climate Projection Database 9

A Method for Municipalities to Enhance Disaster Resilience f...

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9th international conference on Flood Management, ICFM 2023

作者： Suzuki, Akihiro Uemura, Fumihiko Hoshino, Tsuyoshi Yamamoto, Taro Yamada, Tomohito J. Faculity of Engineering Hokkaido University Sapporo Sapporo060-8628 Japan Docon. Co. Ltd. Sapporo060-0042 Japan The Civil Engineering Research Institute for Cold Region Sapporo Sapporo062-7602 Japan Hokkaido River Center Sapporo060-0807 Japan

Issuing appropriate evacuation information is a daunting task for municipalities during unprecedented heavy rainfalls. Due to their limited experience, they will encounter greater challenges in responding appropriately in future climate conditions, where the scale of floods will be greater. In this study, we proposed a method to make flood scenarios based on a climate projection database. We also developed an exercise method for municipalities to consider issuing evacuation information using the scenarios. through a demonstration experiment in Hokkaido, Japan, we conducted a simulation exercise based on the method for a municipality. As a result, they considered specific response and provided valuable insights, thereby enhancing preparedness for potential future flood disaster. Copyright © 2024 Akihiro Suzuki et al.

关键词： Floods

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