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proceedings - 2023 IEEE international conference on Quantum Computing and Engineering, QCE 2023

Proceedings - 2023 IEEE International Conference on Quantum ...

引用

4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

ISBN: (纸本)9798350343236

the proceedings contain 302 papers. the topics discussed include: an empirical comparison of optimizers for quantum machine learning with SPSA-based gradients;towards redefining the reproducibility in quantum computing: a data analysis approach on NISQ devices;crosstalk-based parameterized quantum circuit approximation;a study on concentric feature embeddings in variational multiclass classification for small-scale quantum computers;QubiC 2.0: a flexible advanced full stack quantum bit control system;harnessing the power of long-range entanglement for Clifford circuit synthesis;investigating students’ strengths and difficulties in quantum computing;design of quantum machine learning course for a computer science program;quantum computing, math, and physics (QCaMP): introducing quantum computing in high schools;and leveraging dual enrollment programs to expand secondary education in quantum computation.

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ML Based Phenotype Analysis Using Differential Gene Expression Data in Schizophrenia 4

ML Based Phenotype Analysis Using Differential Gene Expressi...

引用

4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Aadhithyan, D. Shabarirajan, K.J. Maniappan, Varun Shivanesh, B. Divya, S. Praghaadeesh, R. Raja, Kalpana Oviya, I.R. Chennai India School Of Medicine Yale University Section For Biomedical Informatics And Data Science New HavenCT United States

ISBN: (纸本)9798350384628

In this paper, differential gene expression (DGE) data in schizophrenia is analysed using machine learning approaches with the objective of predicting cell phenotypes related to the condition. the dataset contains information on cis eQTNs, gene orientation, probe information, gene expression profiles, and SNP information. When first investigating well-known machine learning methods, a hybrid model combining Random Forests and XGBoost is suggested, however it only offers a little increase. Increases in predicted accuracy of more than 99.12% are made possible by the H2O AutoML package. the results demonstrate the capability of ML-based analysis to unravel the underlying biological mechanisms of schizophrenia and contribute to the design of individualised therapeutic approaches. the paper represents the capability of machine learning to comprehend the relationship between gene expression patterns and complicated illnesses like schizophrenia. © 2023 IEEE.

关键词： machine learning

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Forecasting Air Passengers in Phuket, thailand: An application of machine learning Algorithms 4

Forecasting Air Passengers in Phuket, Thailand: An Applicati...

引用

4th international conference on Big Data Analytics and Practices, IBDAP 2023

作者： Photun, Aphinan thaminkaew, thanakorn Naresuan University Faculty of Science Department of Mathematics Phitsanulok Thailand Bangkok Thailand

ISBN: (纸本)9798350300192

the COVID-19 pandemic occurred in 2020 had a significant impact on the global economy, resulting in a significant decrease in air passengers. As a result, time series trends experienced significant fluctuations that were difficult to predict. the objective of this research was to forecast the number of air passengers in Phuket, thailand, from September to December 2022. Additional features, such as exchange rates and time-based properties, were included to enhance the accuracy of our predictions. We then compared the performance of statistical models and machine learning models using evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Our findings revealed that the SARIMAX model outperformed other models for air passenger forecasting. Nonetheless, machine learning models might be more appropriate for forecasting new data due to their stability with additional training data. this research emphasized the importance of selecting the appropriate model based on the available data and the particular needs of the forecasting problem. © 2023 IEEE.

关键词： Forecasting

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proceedings - 2023 IEEE international conference on Quantum Computing and Engineering, QCE 2023

Proceedings - 2023 IEEE International Conference on Quantum ...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

ISBN: (纸本)9798350343236

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Convolutional Sequence to Sequence learning for English-Khasi Neural machine Translation 4

Convolutional Sequence to Sequence Learning for English-Khas...

引用

4th international conference on Computing and Communication Systems, I3CS 2023

作者： Hujon, Aiusha Vellintihun Amitab, Khwairakpam Singh, thoudam Doren St. Anthony's College Dept. of Computer Science Meghalaya Shillong India North Eastern Hill University Dept. of Information Technology Meghalaya Shillong India NIT Silchar Dept. of Computer Science and Engineering Assam India

ISBN: (纸本)9798350323771

the convolutional neural network is relatively prevalent for visual recognition tasks. Implementing CNN for neural machine translation is still challenging compared to the predominant approach such as recurrent neural networks and transformers. this paper presents an empirical study of the convolutional sequence to sequence learning model to translate English to Khasi. the approach uses convolution for both encoders and decoders with multi-step attention. the study also includes a model using a transfer learning approach on which the parent model is trained using the English-French dataset. the quantitative and qualitative results are significant thus suggesting that the convolutional approach for neural machine translation tasks can perform remarkably well as other tasks using convolution neural networks in the case of English-Khasi language pair. © 2023 IEEE.

关键词： Neural machine translation

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An Efficient Ridesharing Model using machine learning Based on Riders Reviews 4

An Efficient Ridesharing Model using Machine Learning Based ...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Shinde, Abhishek Bhoir, Prajyot Shinde, Sahil Shaikh, Bushra SIES Graduate School of Technology Department of Information Technology Navi Mumbai India

ISBN: (纸本)9798350335750

It is now more important than ever to take action to lessen the negative consequences of private vehicles. If successfully implemented, mass transit is the ideal option, however because of its lack of door-to-door service, lengthier fixed routes, and unreliable timetable, many people do not appreciate it. therefore, new facilities or services should be created to offer users a comfortable and dependable service and to lessen potentially dangerous environmental effects like pollution, congestion, etc. One of the cutting-edge technologies that is being used all over the world is ride sharing, in which users who have the same origin-destination and journey time are matched and share the transport. To help in implementation of ride sharing, a mobile application is being developed using machine learning techniques such as the Naïve Bayes algorithms to match users based on their travel preferences and habits. the app will provide a more personalized and convenient service to the users, ensuring that they are matched with the most suitable carpool partners. © 2023 IEEE.

关键词： machine learning

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Rethink prevalent machine learning attack detection methods from a generalization perspective 4

Rethink prevalent machine learning attack detection methods ...

引用

4th IEEE international conference on Power, Electronics and computer applications, ICPECA 2024

作者： Shi, Mingjie Zhang, Xiao Ruan, Chong Wu, Po Zhang, Chuanfu Luo, Zongfu Deng, Bowen Zhang, Tao School of Systems Science and Engineering Sun Yat-sen University Guangzhou China National Power Dispatching and Control Center State Grid Beijing China State Grid Henan Electric Power Research Institute State Grid Zhengzhou China

ISBN: (纸本)9798350359589

To address attacks from different threat actors and scenarios, the generalizability of network attack detection methods is crucial for critical infrastructure like the power system. machine learning-based attack detection has become the de facto standard solution in this field. However, existing research has primarily focused on scenarios where training and testing distributions are consistent, overlooking the machine learning models' ability to generalize to different distributions. this paper emphasizes two key dimensions: the impact of background traffic and the role of statistical features in the detection process. By embedding identical attack behaviors in different background flows, we reveal significant performance disparities in different traffic scenarios (data domains), highlighting the insufficient generalizability of existing methods. through a comparison between DDoS attacks and XSS attacks, we identify the limitations of relying solely on statistical data for detecting various attack behaviors. Based on the new research perspective proposed in this paper for the attack detection domain, we advocate for adopting a comprehensive approach that integrates both feature and behavior considerations. It involves incorporating security domain knowledge before applying machine learning to enhance the generalization performance of machine learning-based attack detection methods in the complex and dynamic network environment of power systems. © 2024 IEEE.

关键词： Attack Detection Generalization machine learning Power Systems

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GRADIENT PATTERN ANALYSIS APPLIED FOR computer VISION IN MEDICAL ULTRASOUND DIAGNOSIS 17

GRADIENT PATTERN ANALYSIS APPLIED FOR COMPUTER VISION IN MED...

引用

17th international conference on Interfaces and Human computer Interaction 2023, IHCI 2023, the 17th international conference on computer Graphics, Visualization, computer Vision and Image Processing 2023, CGVCVIP 2023 and 16th international conference on Game and Entertainment Technologies 2023, GET 2023

作者： Sautter, Rubens Andreas Rosa, Reinaldo Roberto Alavarce, Debora Cristina Silva, Daniel Guimarães Av. Dos Astronautas 1.758 CEP 12245-027 São José dos Campos SP Brazil Hipocampus EdTech - Digital Learning R. do Serimbura 320 CEP 12243-360 São José dos Campos SP Brazil

ISBN: (纸本)9789898704498

this paper describes a new application of the technique known as Gradient Pattern Analysis (GPA), focused here on computer vision. In the GPA domain, the image is translated into a tessellation triangulation field based on the vectors positions that make up the gradient lattice of the matrix image. the GPA version considered here generates three attributes (G1, G2 and G3) that can be used as labels for a supervised machine-learning model. the case study presented here shows that GPA is a useful tool for real-time fetal biometry from 2D ultrasound images. the application in obstetrics indicates that the technique can also be useful for learning diagnostic imaging in gynecology, hepatology and oncology. the generalization of the technique to other applications in practical learning in health is discussed. © 2023 proceedings of the international conferences on Interfaces and Human computer Interaction 2023, IHCI 2023;computer Graphics, Visualization, computer Vision and Image Processing 2023, CGVCVIP 2023;and Game and Entertainment Technologies 2023, GET 2023. All rights reserved.

关键词： computer vision

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Research on Virtual College English learning Aid System of computer Intelligent Campus 4

Research on Virtual College English Learning Aid System of C...

引用

4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Jin, Xiang Zhao, Zhijian Nanjing Engineering Branch Jiangsu Union Technical Institute Nanjing China

ISBN: (纸本)9798350342710

this paper proposes a virtual system for college English vocabulary assisted learning based on B/S computer. then an image recognition method based on word context vector similarity matching and CNN+BLSTM is proposed to improve the efficiency of word retrieval algorithm. the functional elements and technical requirements of communication AIDS such as recording, storage and playback are realized by using the microcontroller ATmega16L, MMC memory card and VS1003 audio chip. the system includes word learning module, listening learning module, reading and writing module, examination guide and excellent students' secret book module and edutainment module. the application results show that the system can realize the interaction between teachers and students before class, during class and after class, and close the connection between teachers and students. this system improves students' interest in foreign language learning and effectively promotes the improvement of teaching quality. © 2023 IEEE.

关键词： learning systems

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Improved Coronary Artery Disease (CAD) Prediction using machine learning Method with Ensemble Technique 4

Improved Coronary Artery Disease (CAD) Prediction using Mach...

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4th international conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Mehmood, Waqas Khaliq, Abdul Qamar, Zoha Qudsia, Mammona Raza, Syed Saqib Capital University of Science & Technology Computer Science Department Islamabad Pakistan Capital University of Science & Technology Electrical Engineering Department Islamabad Pakistan Institute of Space Technology Electrical Engineering Department Islamabad Pakistan National University of Science & Technology Computer Science Department Islamabad Pakistan

ISBN: (纸本)9798350391862

Predicting Coronary Artery Disease (CAD) presents a critical and intricate challenge within medical science. Late-stage detection of CAD can gravely affect cardiac and vascular health, often leading to obstructions in the veins and perilous conditions. the application of machine learning (ML) in assessing CAD risks is pivotal for the early identification and mitigation of these severe consequences. Our study introduces a voting classifier that integrates eight distinct classification algorithms, employing an ensemble method to refine the accuracy of CAD diagnosis. this approach incorporates supervised learning algorithms such as Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbor (KNN), Support Vector machine (SVM), Decision Trees, Bagging, Boosting, with multi model approach to construct a more generalized and robust predictive framework. A comparative evaluation with existing methodologies was performed to ascertain effectiveness of our proposed model. the experimental findings reveals that our proposed technique markedly enhances diagnostic precision, achieving an accuracy rate of 99% by leveraging the benefits of multi model aggregation in ensemble learning. the high accuracy of our ensemble and more generalized model not only improves diagnostic precision but also holds potential for better patient outcomes by facilitating earlier intervention and treatment. © 2024 IEEE.

关键词： Risk assessment

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