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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3243 条记录，以下是671-680 订阅

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Intelligent water quality monitoring using WSN and machine learning Approaches 23

Intelligent water quality monitoring using WSN and Machine L...

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7th international conference on Information System and Data Mining, ICISDM 2023

作者： Belachew, Ermiyas Birihanu Bekele, Mebiratu Beyene Birawo, Birtukan Adamu Elte Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Hungary Department of Software Engineering Wolkite University Ethiopia Bahir Dar Institute of Technology Ict 4D Research Centre Bahir Dar University Ethiopia Applied Informatics Silesian University of Technology Poland

ISBN: (纸本)9798400700637

In recent years, wireless sensor networks (WSN) have received considerable attention in environmental, industrial monitoring, and control applications. It is used to monitor, collect, maintain, and analyse environmental data from a remote place with the help of remote sensing and monitoring technologies. Identification of suitable algorithms and protocol WSNs for water quality monitoring is a challenge due to the limitation of energy, computation capability, and storage resources of the sensor nodes. In this paper, we designed a model for a WSN-based water quality monitoring system that employs a machine learning technique to analyse water data and facilitates decision-making. The suggested model can gather water quality parameters from a water body and send them to a distant data centre, where they will be processed using a machine learning (ML) algorithm. The experiment shown that by adopting WSN and ML approach, we can obtain state-of-the-art 0.0723 RMSE and 0.0052 MAE, indicating that the proposed methodology is appropriate for usage in intelligence water quality monitoring. © 2023 Owner/Author.

关键词： Water quality

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Enhancing the Explainability of Deep learning Based Malware Detection System 9

Enhancing the Explainability of Deep Learning Based Malware ...

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9th Annual international conference on Network and Information Systems for Computers, ICNISC 2023

作者： Fan, Haoran Yang, Yulong Lin, Chenhao Li, Qian Xi'an Jiaotong University School of Software Engineering Xi'an China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China

ISBN: (纸本)9798350306682

With the rapid development of deep learning, deep learning-based malware detection has received increasing attention because of its advantage of not relying on domain knowledge. The research community has proposed some rudimentary methods to enhance the explainability of machine learning-based malware classifiers. However, these studies lack an analysis on the explainability of DNN. Research on the explainability of DNN is more challenging because of its complexity. In this work, we first propose a feature extraction method for code data, so that the deep learning model can capture the logical structure of the code. Then we train multiple deep learning models based on source code data, including MLP, CNN, and RNN. Finally, we construct feature attribution analysis for them based on local linear approximation and feature attribution respectively, to give the important features on which the inference decision of the deep neural network malicious code detection model relies, and realize the interpretability enhancement. The experimental results of the explainable analysis show that the explainable method in this paper can capture the important features and help users analyze malware better. © 2023 IEEE.

关键词： Feature extraction

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Cryptic binding site prediction with protein language models

Cryptic binding site prediction with protein language models

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Škrhák, Vít Riedlova, Kamila Novotný, Marian Hoksza, David Charles University Faculty of Mathematics and Physics Department of Software Engineering Prague Czech Republic Charles University Faculty of Science Department of Cell Biology Prague Czech Republic

ISBN: (纸本)9798350337488

Structure-based identification of protein-ligand binding sites plays a crucial role in the initial stages of rational drug discovery pipelines. As machine learning methods are increasingly integrated into the process, a significant challenge arises while training these methods, as labeled data are typically derived from ligand-bound structures. Consequently, these methods struggle to detect binding sites within proteins where the binding site is concealed in the absence of a bound ligand. Here, we explore the possibility of harnessing protein language models to address this issue and compare their performance against state-of-the-art methods, both those specialized in the cryptic binding site (CBS) detection and those that are not. We show that applying pre-trained protein-language models in a relatively straightforward manner enables us to surpass the state-of-the-art of CBS prediction. © 2023 IEEE.

关键词： cryptic binding site deep learning machine learning neural network protein language model

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machine learning Hardware Implementation of Handwritten Digit Inference using Arduino and Ternary Output Binary Neural Network

Machine Learning Hardware Implementation of Handwritten Digi...

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2023 IEEE international conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Ahn, Seongmin Lee, Jaehyeok Na, Taehui Incheon National University Department of Electronics Engineering Korea Republic of

ISBN: (纸本)9798350344318

In this paper, we analyze binary neural network (BNN) and ternary output BNN (ToBNN) from a software perspective, and introduce tiny machine learning (TinyML) hardware implementation of handwritten digit inference. Both BNN and ToBNN achieve a reduction of approximately 70% in memory usage for weight storage by using binary values. Inference energy consumption was also reduced by approximately 32%. ToBNN achieves 90.24% inference accuracy for MNIST dataset, while conventional BNN shows 50.25% accuracy. To implement TinyML hardware, Arduino Uno, Portenta H7, TFT touch LCD shield, and 16×2 LCD were used. The hardware implementation results show successful inference for handwritten digits. © 2023 IEEE.

关键词： Energy utilization

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Reconstructing Reverse Innovation and Expansions

Reconstructing Reverse Innovation and Expansions

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2024 IEEE international conference on Industrial engineering and engineering Management, IEEM 2024

作者： Kim, Song-Kyoo Macao Polytechnic University China

ISBN: (纸本)9798350386097

The expansion of reverse innovation is addressed in this paper by reconstructing the concept and demonstrating cases to illustrate how it can be applied in areas beyond the development of physical products. Reverse innovation stands in contrast to the traditional approach of creating products for advanced economies. This new concept of reverse innovation has gained popularity and holds potential for broader applications since 2009. The first component of the paper involves redefining the concept of the reverse innovation to enhance understanding. Additionally, the second part focuses on expanding its applications into different industry sectors. A study case in a TV program demonstrates how the reverse innovation can be applied in the content industries, while another case provides insights into applying the reverse innovation in software engineering and machine learning studies. © 2024 IEEE.

关键词： innovation diffusion innovation strategy local innovation media contents Reverse innovation software engineering TV reality show value innovation

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Fully Convolutional Generative machine learning Method for Accelerating Non-Equilibrium Green's Function Simulations

Fully Convolutional Generative Machine Learning Method for A...

引用

international conference on Simulation of Semiconductor Processes and Devices (SISPAD)

作者： Aleksandrov, Preslav Rezaei, Ali Xeni, Nikolas Dutta, Tapas Asenov, Asen Georgiev, Vihar Univ Glasgow James Watt Sch Engn Device Modelling Grp Glasgow Lanark Scotland

ISBN: (纸本)9784863488038

This work describes a novel simulation approach that combines machine learning and device modeling simulations. The device simulations are based on the quantum mechanical non-equilibrium Green's function (NEGF) approach and the machine learning method is an extension to a convolutional generative network. We have named our new simulation approach ML-NEGF and we have implemented it in our in-house simulator called NESS (nano-electronics simulations software). The reported results demonstrate the improved convergence speed of the ML-NEGF method in comparison to the 'standard' NEGF approach. The trained ML model effectively learns the underlying physics of nano-sheet transistor behaviour, resulting in faster convergence of the coupled Poisson-NEGF simulations. Quantitatively, our ML-NEGF approach achieves an average convergence acceleration of 60%, substantially reducing the computational time while maintaining the same accuracy.

关键词： machine learning neural network autoencoder device simulations and modeling non-equilibrium Green's function (NEGF) TCAD device modeling nanowires

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A Supervised machine learning Method for Predicting the Employees Turnover Decisions 8

A Supervised Machine Learning Method for Predicting the Empl...

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8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Arromrit, Trirat Srisakaew, Korrapin Roswhan, Napatsakorn Mahikul, Wiriya Princess Srisavangavadhana College of Medicine Chulabhorn Royal Academy Bangkok Thailand

ISBN: (纸本)9798350310931

Human resource management is a crucial component for the smooth operation of business organizations. However, changing perspectives and values among different age groups present significant challenges for organizations. Resignation and quick job changes can lead to huge budgetary and manpower issues. This research aims to develop and compare machine learning algorithms for predicting employees' turnover decisions using a public dataset on *** The dataset includes performance data for 15,000 employees. The eight features and five algorithms, including Random Forest, Support vector machine, Logistic Regression, K-Nearest Neighbor, and Gaussian Naïve Bayes classifier, were employed to develop the model. The results showed that the Random Forest Classifier was the most efficient model with Geometric Mean, F1-score and AUC which are 0.995, 0.995, and 0.999, respectively when evaluated with stratified K-fold cross-validation. Moreover, using the SHapley Additive exPlanations method, five important factors for predicting employee turnover were identified. These factors include satisfaction level, average working hours in a month, number of responsibility projects, time spent with the company and salary level. These features enable the Human Resources department to gain valuable insights, facilitating strategic planning and proactive measures to prevent employee resignations. By doing so, businesses can achieve smoother and more efficient operations. © 2023 IEEE.

关键词： Support vector machines

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Predicting the Performance of Electrical machines using machine learning 8

Predicting the Performance of Electrical Machines using Mach...

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8th international conference on Information Technology Research, ICITR 2023

作者： Manohar, V Joshi Jha, Sumit Kumar Presidency University Department of Electrical & Electronics Engineering Bengaluru India Indian Institute of Technology School of Electrical Science Odisha Bhubaneswar India

ISBN: (纸本)9798350359503

Electrical machines play an important role in our day-to-day life. Electric machines like DC motors and 3- phase induction motors are essential systems and widely used in domestic, industrial and transportation systems. In order to operate the machines optimally and efficiently, in real time operations, it is required to predict the performance parameters at various loaded conditions. With the advancements in the field of predictive modelling and analytics, several researchers have applied in the area of energy consumption prediction, fault prediction, weather prediction, power grid management and so on. In this paper, the machine learning techniques are demonstrated that may be used to examine the performance of electrical machinery by forecasting performance characteristics like speed and efficiency. To validate the performance of the predictive model, an experiment was conducted at the laboratory on dc motor and 3-phase induction motor to generate the required dataset to train the regression algorithms. The model evaluation metrics such MSE and the R2 value showed that the model efficiently predicted the performance of the electrical machines. © 2023 IEEE.

关键词： Python

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A Review of Knowledge Graph Embedding Methods of TransE, TransH and TransR for Missing Links 8

A Review of Knowledge Graph Embedding Methods of TransE, Tra...

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8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Asmara, Salwana Mohamad Sahabudin, Noor Azida Nadiah Ismail, Nor Syahidatul Ahmad Sabri, Ily Amalina Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9798350310931

Knowledge representation and reasoning require knowledge graph embedding as it is crucial in the area. It involves mapping entities and relationships from a knowledge graph into vectors of lower dimensions that are continuous in nature. This encoding enables machine learning algorithms to effectively reason and make predictions on graph-structured data. This review article offers an overview and critical analysis specifically about the methods of knowledge graph embedding which are TransE, TransH, and TransR. The key concepts, methodologies, strengths, and limitations of these methods, along with examining their applications and experiments conducted by existing researchers have been studied. The motivation to conduct this study is to review the well-known and most applied knowledge embedding methods and compare the features of those methods so that a comprehensive resource for researchers and practitioners interested in delving into knowledge graph embedding techniques is delivered. © 2023 IEEE.

关键词： Knowledge graph

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A Novel Model Using Multiple Bagging Ensemble Method For Measuring, Inferring and Predicting the Quality of Continuous Assessment Question Papers 14

A Novel Model Using Multiple Bagging Ensemble Method For Mea...

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Senthil Velan, S. Preethika Reddy, S. Talwar, Preethi Bheemshankar Gomathi, R.D. CMR Institute of Technology Information Science and Engineering Bengaluru India Kongu Engineering College Department of English Tamil Nadu India

ISBN: (纸本)9798350335095

Predictive machine learning techniques provide mechanisms for the computing machines to analyze and understand the knowledge inherently embedded in the given dataset. This unique technique can be effectively used in understanding the quality of the question papers compiled for Continuous Assessment Tests (CATs). Understanding the quality of the question papers requires a systematic and software engineering-based approach. In this research work, a few of the CAT question papers have been carefully picked and used for applying the proposed methodology. Well, defined set of features, properties, and quality attributes have been applied in the proposed model and mapped into each other for providing a better understanding of the question paper's quality. Using metrics the analysis of quality for the given CAT paper is attempted. Based on the application of the model, it was able to clearly predict and understand the quality levels of the given set of question papers. © 2023 IEEE.

关键词： software engineering

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