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Interpreting Accuracy: A Comprehensive Analysis of AI models for Liver Disease Diagnosis 6

Interpreting Accuracy: A Comprehensive Analysis of AI Models...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Dalvi, Harshal Narvekar, Meera Dalvi, Prachi Vadgama, Kunaal Kasmani, Momin Ranawat, Krish Dept. of Computer Engg DJSCE Mumbai India Dept. of AI&DS Fr. CRCE Mumbai India Dept of Info.Tech DJSCE Mumbai India

ISBN: (纸本)9798350369748

It is critical to make a timely diagnosis of liver disease to avoid fatal health complications. the use of AI-based diagnosis systems can help in the early detection of such liver diseases based on health parameters. However, the use of such systems is not yet widely accepted by medical practitioners due to their complex nature and inability to comprehend the rationale behind the outcomes. In this research article, we attempted to identify the relationship between interpretability and accuracy in machine learning models applied for diagnosing liver disease using a variety of models, such as Logistic Regression, KNN, decision tree, XGBoost, and random forest. We performed a thorough evaluation of a dataset of liver patients and analyzed traditional performance metrics, such as accuracy, recall, precision, and F1 score, to provide a thorough insight into the diagnostic capabilities of each model. Moreover, we use SHAP (SHapley Additive exPlanations) values to decipher the underlying characteristics that propel the predictions of the models. the goal of this interpretability analysis is to clarify the decision-making process and provide important clinical practitioner insights. By carefully analyzing interpretability and accuracy, we hope to uncover possible trade-offs and offer insightful advice for using these models in actual healthcare environments. this work has immediate implications for clinical practice in addition to adding to the growing body of knowledge in medical diagnostics and machine learning. the information provided here aims to improve patient care and outcomes by strengthening the validity and efficacy of liver disease diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

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Text error correction after text recognition based on MacBERT4CSC 6

Text error correction after text recognition based on MacBER...

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6th international conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2023

作者： Guan, Yanzhi Pang, Ziliang Ding, Yehui Tian, Longlong College of Mechanical and Materials Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9781510668249

With the wide application of text recognition technology, the text recognition model's own limitations and environmental factors interfere with the recognition error rate is high. To address the above situation, a text error correction method based on MacBERT4CSC after text recognition is proposed. Firstly, using the single-word text recognition confidence, we obtain the suspected wrong words and positions by setting thresholds for error detection of the text to be corrected, and then iterate through the constructed confusion set for text error correction in priority, and after the traversal is completed, the MacBERT4CSC model recalls the candidate words with suspected wrong positions, and finally, after the similarity of the word code and the MacBERT4CSC model score meet the set conditions, the error correction is finally completed by sorting the candidate words that meet the conditions. By imitating the error type of word recognition to build the dataset for word recognition and calling Ali's word recognition API for word recognition to obtain the test set to be corrected, the comparison experiments show that the MacBERT4CSC recall and word bar code sorting method improves in accuracy, recall rate and F1 value compared with other deep models and traditional rule error correction methods, which verifies the effectiveness of the method. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Error correction

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A feature integration method for Chinese chunking 6

A feature integration method for Chinese chunking

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6th international conference on Computer Information Science and Application Technology, CISAT 2023

作者： Lyu, Chen Xu, Xuejing Huang, Jiangping School of Interpreting and Translation Studies Guangdong University of Foreign Studies Guangzhou510420 China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781510668546

Chunking is a crucial task in natural language processing. the task aims to divide a text into syntactically correlated non-overlapping chunks. the discrete model and neural model can be applied to Chinese chunking. the feature representations of them are different and both can achieve excellent performance. In this paper, we build both of the models and make a detailed comparison between the two models. Furthermore, we present the feature integration method to integrate the advantages of the two models. Experiments show the effectiveness of the feature integration method. Since the neural model utilizes pre-trained word embeddings, it can be regarded as a semi-supervised learning method. In order to make a fairer comparison between the discrete model and neural model, we incorporate word clusters into these models. Experimental results show that the word cluster information does not significantly improve chunking performance and the feature integration method still improves the performance of both the discrete model and neural model. © 2023 SPIE.

关键词： Learning algorithms

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Heat Technology Installations Simulation in the Educational Process 6

Heat Technology Installations Simulation in the Educational ...

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6th international conference on Information Technologies in engineering Education, Inforino 2022

作者： Popov, S.K. Vaniushkin, V.D. Valineeva, A.A. National Research University MPEI Moscow Russia

ISBN: (纸本)9781665405775

the research focuses on the development of mathematical models of processes and installations of industrial thermal power engineering, as well as the presentation of software products created in Mathcad and Microsoft Visual C++ codes. A significant part of Mathcad programs is freely available on the Internet as part of four blocks. the first block "Heat engineering tasks"presents programs for calculating the material balance of the complete combustion of gaseous and condensed fuels;characteristics of the combustion process based on the results of the exhaust gases composition analyses;the material balance of the glassmaking process;determination of the equilibrium composition of the incomplete combustion products of fuel, gasification of solid fuel, steam reforming of natural gas. the block contains programs for calculating the temperature characteristics of the combustion process-calorimetric and theoretical fuel combustion temperatures and provides the possibility of computational analysis of the thermal balance of a fuel high-Temperature installation. Programs of approximation of experimental and computational data by an arbitrary analytical function are presented. the second block of programs "Gasification plant"contains software implementation of mathematical models and installations of solid fuel gasification processes. the third block of programs "Conversion"combines programs for calculating the processes of exhaust gases heat thermochemical recuperation from high-Temperature industrial plants-A promising direction of fuel economy. these programs are used in the study of energy saving issues in industry. the fourth block of "Hydrogenium"programs is focused on the study of hydrocarbon conversion processes that play an important role in hydrogen energetics. the opportunity to study the efficiency of utilization of thermal and material waste of high-Temperature installations to reduce the energy intensity of hydrogen production is provided. Studying the processe

关键词： Visualization Power engineering Temperature Heat engines thermal engineering C++ languages software

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6th international conference on Innovative Computing and Communication, ICICC 2023

6th International Conference on Innovative Computing and Com...

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6th international conference on Innovative Computing and Communication, ICICC 2023

ISBN: (纸本)9789819933143

the proceedings contain 128 papers. the special focus in this conference is on Innovative Computing and Communication. the topics include: Wearable Sensor Based Cloud Data Analytics Using Federated Learning Integrated with Classification by Deep Learning Technique;comparison of Decision Tree and Random Forest for Default Risk Prediction;trusted Cloud Service Framework for Cloud Computing Security;HITR-ECG: Human Identification and Classification Simulation System Using Multichannel ECG Signals: Biometric Systems Era;brain Disorder Classification Using Deep Belief Networks;Diabetic Retinopathy Detection Using Deep CNN Architecture and Medical Prescription;an Innovative software engineering Approach to Machine Learning for Increasing the Effectiveness of Health Systems;region of Interest and Feature-based Analysis to Detect Breast Cancer from a Mammogram Image;vehicular Ad Hoc Network: A Review;Securing the Networks Against DDoS Attacks Using Blockchain Technology;the Proposed Deep Learning Combined with Knowledge Graphs for Fake News Detections on Social Networks;algorithm-Driven predictive Analysis of Blue-Chip Stocks in the Murky Indian Environment;survey on Crime Analysis, Forecasting, and Prediction Using Machine Learning;soft Computing and Data Mining Techniques for Dengue Detection: A Review;Extended HD-MAABE Scheme Supporting Delegation and Revocation for Cloud-based Organizational Data Sharing;detection of Open Metal Sites in Metal–Organic Frameworks Using Machine Learning;PCA-Based Feature Selection and Hybrid Classification Model for Speech Emotion Recognition;ANFIS and Kernel Extreme Learning Machine to the Assessment and Identification of Seismic b-value as Precursor;payroll Management Using Blockchain;centric Management of Resources in Modern Distributed Computing;enhancing Security of IoT Data Transmission in Social Network Applications.

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6th international conference on Innovative Computing and Communication, ICICC 2023

6th International Conference on Innovative Computing and Com...

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6th international conference on Innovative Computing and Communication, ICICC 2023

ISBN: (纸本)9789819930098

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Assessing Uncertainty in Space Weather Forecasting Using Quantile Regression and Complex Nonlinear Systems Identification Techniques 6

Assessing Uncertainty in Space Weather Forecasting Using Qua...

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6th international conference on Computing, Electronics and Communications engineering, iCCECE 2023

作者： Wei, Hua-Liang Balikhin, Michael A. Boynton, Richard J. Walker, Simon N. The University of Sheffield Dept. of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350340921

Space weather forecasting is of global interest, and its importance is well established in research community and recognized by government, industries and stockholders. Over the past years, many types of predictive models have been developed in the literature. there is a general agreement that forecasting models should not only provide point prediction but also inform the uncertainty associated with the prediction. this study presents a novel method bases on quantile regression and complex dynamic modelling for measuring uncertainties in space weather forecasting. the approach is implemented using Quantile regression and Nonlinear AutoRegressive Moving Average with Exogenous inputs (NARMAX) methods (for short the approach is called Q-NARMAX). the method is applied to Disturbance storm index (Dst) observations to examine its interpretability and capability for uncertainty analysis. Results show that the proposed Q-NARX model can produce excellent predictions of the Dst index, and meanwhile provides a measure for assessing the uncertainty in the forecast. the innovative integration of quantile regression, complex dynamic modelling and nonlinear system identification techniques enables the proposed work to have following attractive advantages and properties: 1) it can produce excellent prediction accuracy for space weather forecasting, 2) it uses transparent models to approximate (represent) black-box systems, enabling to interpret the dependent relationship between space weather indices (system outputs) and their drivers (system inputs), and 3) more importantly, it allows for uncertainty assessment and analysis of models and forecasts. © 2023 IEEE.

关键词： Uncertainty analysis

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Research on Photovoltaic Power Generation Power Prediction Based on RF-XGBoost Model 6

Research on Photovoltaic Power Generation Power Prediction B...

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2023 IEEE 6th international conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Zhou, Mingjie Li, Xufeng Zhong, Zuyu Sichuan University College of Software Engineering Chengdu China South China Normal University Department of Mathematical Sciences Guangzhou China Fuzhou University Maynooth International Engineering College Fuzhou China

ISBN: (纸本)9798350313444

With the increasing proportion of photovoltaic power generation in the power system year by year, the prediction of photovoltaic power generation power is increasingly valued. this article proposed a photovoltaic power generation power prediction method based on the RF-XGBoost model to address the issue of unstable output power of photovoltaic power generation systems when integrated into the power grid, using meteorological data for prediction. this method combined the Random Forest algorithm (RF) and XGBoost algorithm, and fused their prediction results by weighted average, giving full play to the advantages of the two models, thus improving the accuracy and reliability of photovoltaic power prediction. through the training and verification of historical data, the experimental results have shown that the RF-XGBoost model is superior to the single XGBoost model, Random Forest model and RF-SVR model, and has achieved remarkable results in prediction accuracy and performance advantages. this method has provided reliable decision-making support for the optimized operation of photovoltaic power generation systems and grid management, and had an important contribution to promoting the large-scale application of photovoltaic power generation. © 2023 IEEE.

关键词： model fusion Photovoltaic power generation power prediction Random Forest XGBoost

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Application of Recurrent Neural Networks to Covariate software Defect Prediction 28

Application of Recurrent Neural Networks to Covariate Softwa...

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28th ISSAT international conference on Reliability and Quality in Design, RQD 2023

作者： Salboukh, Fatemeh Silva, Priscila Nagaraju, Vidhyashree Fiondella, Lance Engineering and Applied Science University of Massachusetts Dartmouth MA United States Electrical and Computer Engineering University of Massachusetts Dartmouth MA United States Department of Computer Science Stonehill College MA United States

ISBN: (纸本)9798986576121

Traditional software reliability growth models (SRGM) assess defect discovery as a function of testing time or effort to quantify failure intensity based on the Non-Homogeneous Poisson Process (NHPP). More recently, covariate NHPP models have substantially improved defect prediction by characterizing software defect discovery in terms of underlying testing activities. Traditional NHPP models are based on parametric forms with specific distributional assumptions, which often fail to capture all details present on defect data. therefore, this paper assesses the effectiveness of neural networks to predict software defects including covariates to encode factors driving defect discovery explicitly. Two neural networks are considered, including recurrent neural networks (RNNs) and long short-term memory (LSTM), which are then compared with the traditional covariate models to validate predictive capability. Our results suggest that LSTM achieves better overall goodness-of-fit measures, such as approximately 6 and 9 times smaller mean square errors and mean absolute percentage error, respectively, compared to traditional models when 75% of the data is used for training. these results suggest that neural networks are able to track and predict defect trends more accurately than traditional methods. © RQD 2023. All rights *** right reserved.

关键词： software reliability

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Digitization and Digitalization of Cost and Duration Estimation in Philippine Public School Building Projects Using Artificial Neural Network 6

Digitization and Digitalization of Cost and Duration Estimat...

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6th international conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Aguinaldo, Ermengard S. Silva, Dante L. Mapúa University School of Graduate Studies Manila City Philippines Mapúa University School of Civil Environmental and Geological Engineering Manila City Philippines

ISBN: (纸本)9781665491259

Digital technology and innovation integration in the construction sector have demonstrated their effectiveness in boosting workplace productivity. the construction business has benefited from the use of computer-aided design (CAD) software, which significantly decreased project cost and length while maximizing efficiency. Estimating cost and duration (CD) during the pre-construction phase of a project is critical due to the limited available data during this phase. Utilization of machine learning (ML) methods, such as artificial neural network (ANN), are now evident to solve complex problems even with minimal data available. At the pre-construction stage of a construction project, this study created a two-phase ANN model that can forecast the project cost and duration for public school buildings in the Philippines based on the data provided. the ANN model was initially composed of multiple sub-models that will form a final comprehensive model, wherein the output of the sub-models will become the input of the final model. the sub-models were based on the determined Work Breakdown Structure (WBS) of the project, namely earthworks, structural, finishing, plumbing and sanitary, electrical, and mechanical. Based on the excellent performance of the input parameters (IP), its structure gave a completer and more understandable ANN model. the contribution of the input parameters available at the preconstruction stage of a construction project to the overall project cost and overall project duration was also determined using sensitivity analysis (SA). © 2023 IEEE.

关键词： Sensitivity analysis

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