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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4179 条记录，以下是601-610 订阅

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Advancement of Drug Discovery Using Artificial Intelligence and machine learning

Advancement of Drug Discovery Using Artificial Intelligence ...

引用

2024 IEEE international conference on Computing, applications and Systems, COMPAS 2024

作者： Nilima, Sadia Islam Hossain, Md Azhad Sharmin, Sadia Rahman, Rukshanda Esa, Hammed Manik, Mia Md Tofayel Gonee Hasan, Rakibul International American University Department of Business Administration 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States International American University Department of Information Technology Wilshire Blvd STE 1000 Los AngelesCA90010 United States Westcliff University Department of Computer Science 17877 Von Karman Ave 4th Floor IrvineCA92614 United States Westcliff University Department of Business Administration 17877 Von Karman Ave 4th Floor IrvineCA92614 United States

ISBN: (纸本)9798331529765

the current boom in artificial intelligence and machine learning has significantly reduced human labor and greatly improved quality of life. this paper explores how AI and ML are being applied to streamline drug discovery and development, enhancing both efficiency and accuracy. Nevertheless, the limited effectiveness, distribution to unintended targets, time-consuming nature, and exorbitant expenses provide significant obstacles and difficulties that affect the process of drug design and development. the study involved an extensive literature review of numerous research articles and effectively highlight and discuss about the application of various advanced algorithms for drug discovery. the study highlighted how AI and ML contribute to reducing the time and cost associated with drug development and discovery. these technologies allow for the use of simulations, which can potentially obviate the need for clinical trials, and enable researchers to conduct extensive studies on various compounds without experimental trials. the results illustrate the extensive application of AI and ML in drug discovery, indicating a promising future for these technologies. they hold significant potential for further exploration and application in drug discovery and development by researchers, students, and the pharmaceutical industry. Finally, the progress in AI and deep learning presents a promising chance for the rational development and exploration of drugs, ultimately benefiting humanity. © 2024 IEEE.

关键词： Drug discovery

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Harnessing the Power of Multimodal Physiological Signals and Deep learning for Stress Monitoring 4

Harnessing the Power of Multimodal Physiological Signals and...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Bv, thanush Athota, Ravi Kumar Divya Varshini, A. Radha Komalidevi, M. Lakshman, D. Sumathi, D. School of Computer Science Engineering VIT-AP University Andhrapradesh Amaravathi522237 India

ISBN: (纸本)9798350306118

Physiological and behavioral information obtained from wearable or mobile sensors has been utilized to estimate self-reported stress levels. However, the reliance on self-reports for stress annotation during studies often leads to a scarcity of labeled data, impeding the development of accurate and broadly applicable stress prediction models. In contrast, sensors can continuously capture signals without requiring annotations. Researchers have endeavored to identify stress using conventional machine learning techniques for analyzing physiological signals. the achieved results, which vary between 50% and 90% accuracy, have produced mixed outcomes. A drawback of traditional machine learning algorithms is their dependency on manually crafted features. Accuracy tends to decline if these features are misidentified. To overcome this limitation, we introduced a deep neural network approach. Deep neural networks eliminate the need for hand-crafted features and instead extract pertinent features directly from raw data through the network's layers. In our study, deep neural networks were employed to analyze physiological data obtained from the DRYAD dataset. the objective encompassed multi class classification for stress detection, where the networks differentiated among low-stressed, medium-stressed, and high-stressed states, effectively distinguishing baseline, stressed, and amused states. © 2023 IEEE.

关键词： Deep neural networks

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A Comprehensive Taxonomy of Adversarial Attacks on machine learning in IoT application 7

A Comprehensive Taxonomy of Adversarial Attacks on Machine L...

引用

7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Praveena, M. Madhumitha, S. Menakadevi, J. Lalith Akkash, V. Dr. SNS Rajalakshmi College of Arts and Science Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350340600

this research is a comprehensive taxonomy of threats against Internet of things (loT-)based machine learning (ML) systems. As the number of IoT applications continues to surge, protecting machine learning models from malicious attacks is more essential than ever. this study categorizes various attack techniques, ranging from white-box to black-box, evasion to poisoning, and beyond, to provide an overview of the evolving security environment within IoT systems. the proposed study will provide attack techniques, evaluation metrics, defense mechanisms, and case studies to shed additional light on securing Internet of things (IoT) devices. this research contributes to understanding how to use machine learning safely in the Internet of things by analyzing the prevalence and severity of attacks, the effectiveness of countermeasures, and the effect on system performance. © 2023 IEEE.

关键词： Internet of things

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Enhancing Symbol Recognition Accuracy and Efficiency through machine learning Approach 4

Enhancing Symbol Recognition Accuracy and Efficiency Through...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Singh, Harkirat Dixit, Prachiv Singh, Gurwinder Chandigarh University Department of AIT-CSE Punjab India

ISBN: (纸本)9798350306118

the recognition of symbols holds significant importance in a diverse array of applications, including but not limited to visual character recognition, object recognition, and data analysis. this paper's main aim is to improve the precision and effectiveness of symbol identification by employing a machine learning methodology. In this discourse, we delve into the obstacles encountered in the realm of symbol identification, encompassing issues such as the presence of diverse shapes, potential confusion between symbols that bear resemblance, the interference caused by image noise, the scarcity of available training data, and the computational intricacy involved. In order to tackle these issues, we suggest employing machine learning methods, particularly deep learning models, to enhance the performance of symbol identification. In this study, we investigate various methodologies, including data augmentation, image pre-processing, and the incorporation of extensive training datasets. Furthermore, we examine the hardware and software modifications that enable the optimisation of symbol recognition processing. the effectiveness of the proposed methodology in enhancing accuracy and efficiency in symbol identification tasks is substantiated through rigorous experimentation and evaluation. the results reported in this study make a valuable contribution to the progress of symbol identification methodologies, hence facilitating the development of more resilient and dependable systems across diverse fields. © 2023 IEEE.

关键词： Object recognition

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application of machine learning Algorithms in Medical Image Analysis: A case study for Breast Cancer detection 10

Application of Machine Learning Algorithms in Medical Image ...

引用

10th international conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Tripathi, Aashi Anwar, Shamama Birla Institute of Technology Mesra Department of Computer Science and Engineering Ranchi835215 India

ISBN: (纸本)9781665490993

Image processing is a very vital part of many medical diagnosis. With the advent of more technologically advanced devices, machine learning implementation has also proven to be boon in the medical world for imaging related diagnosis. this paper aims to utilize different machine learning models to highlight its efficacy in the field of medical image analysis. the paper uses machine learning classifiers to classify breast cancer into malignant and benign using 31 attributes of the Wisconsin Breast Cancer Diagnostic dataset. Five different classifier models - Decision Trees, SVM, Naive Bayes, KNN and ANN were used to classify the tumors and it was observed and concluded that SVM model performed better with an accuracy of approximately 97.4% followed by ANN model with an accuracy of approximately 96.5%. © 2023 IEEE.

关键词： Medical imaging

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DLMUSIC: the Improved MUSIC Algorithm Based on Deep learning 4

DLMUSIC: The Improved MUSIC Algorithm Based on Deep Learning

引用

4th international conference on computer Engineering and application, ICCEA 2023

作者： Bai, Yin Chen, Zhimin School of Electronic and Information Shanghai Dian Ji University Shanghai China

ISBN: (纸本)9798350347548

Direction of arrival (DOA) estimation is an important problem in both wireless communication and radar applications. this paper proposes a new algorithm combining deep learning with multiple signal classification (MUSIC), called DLMUSIC. We use multiple convolutional neural networks (CNNs) to estimate the covariance matrix, and then take the eigenvalue decomposition of the covariance matrix instead of the empirical covariance matrix, meanwhile we use the spatial spectrum approximated by Gaussian function as the label of training data instead of MUSIC spectrum, so our algorithm can overcome many limitations of the MUSIC algorithm which include the inability to localize coherent sources and the poor performance at low signal-to-noise ratio (SNR). Simulation results demonstrate that the DLMUSIC algorithm perform much better than MUSIC algorithm at low SNR with a few snapshots. © 2023 IEEE.

关键词： Multiple signal classification

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12th EAI international conference on Context-Aware Systems and applications, ICCASA 2023

12th EAI International Conference on Context-Aware Systems a...

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12th EAI international conference on Context-Aware Systems and applications, ICCASA 2023

ISBN: (纸本)9783031588778

the proceedings contain 14 papers. the special focus in this conference is on Context-Aware Systems and applications. the topics include: User-Based Collaborative Filtering Multi-criteria Recommender System Based on Interaction Between Criteria, Criteria Set with Choquet Integral;application of machine learning Techniques to Classify Intention to Pay for Forest Ecosystem Services;Anomaly Detection in Univariate Time Series: HOT SAX vesus LSTM-Based Method;application of machine learning Models for Predicting Glucose-Level in the Pure Fluid with Algorithm for Reducing Data Dimension Based on Data Series Extraction;comprehensive Survey On Remote Sensing Image Processing Techniques for Image Classification;item-Based Energy Clustering Recommendation;General Evaluation of EtherCAT-Based Techniques in Various Industrial Systems: Review and applications;towards an IoT-Based Unmanned Surface Vehicle Design for Environment Monitoring in Mekong Delta;3D CNN with BERT and Vision Transformer for Video Recognition;Identify Tumors on Lung CT Images;a Context-Aware application to Monitor the Air Quality;applying Guided Discovery learning to Enhance the Achievement of Information Technology Team.

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Conversational machine Reading Comprehension Based on Complete History Information with Flow 4

Conversational Machine Reading Comprehension Based on Comple...

引用

4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Huang, Chenkai Song, Miao College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781510672505

Conversational machine reading comprehension (CMRC) requires models to effectively combine dialogue history and answer current questions. Previous works have shortcomings in handling historical information as they did not consider the role of historical questions in the learning process. Moreover, in the reasoning process, parallel input of multiple rounds of dialogue does not conform to human reasoning habits. therefore, to address these limitations, this paper proposes the HistoryintoFlow model. In our model, we incorporate historical questions into the encoding layer, which enables the model to extract complete historical information. In the reasoning layer of the model, we design a flow module that integrates intermediate representations generated from past conversations and performs reasoning in accordance with the order of conversations. the final results show that the HistoryintoFlow model achieves an accuracy rate of 67.1% on the QuAC. Compared with some publicly available models, our model has improved in F1, HEQ-Q, and HEQ-D. © 2023 SPIE.

关键词： learning systems

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Comparative Evaluation of machine learning Algorithms for Detecting Credit Card Fraud 8

Comparative Evaluation of Machine Learning Algorithms for De...

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8th international conference on Communication and Electronics Systems, ICCES 2023

作者： Prashanth, Banagani Gangadhar, Suddapalli Bhardwaj, Vivek Reddy, Kovvuru Diwakar Reddy, Puli Shiva Mehra, Manik Lovely Professional University Department of Computer Science and Engineering Phagwara India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India Lovely Professional University School of Computer Application Phagwara India

ISBN: (纸本)9798350396638

Detecting credit card fraud is an extremely important issue in the financial sector, as it results in significant financial losses and negatively impacts customer trust. the main motive of CCFD is to develop methods to identify fraudulent transactions accurately and efficiently. this research typically involves the use of statistical and machine-learning algorithms to analyze large amounts of transaction data. Utilizing machine learning is a potential solution for addressing credit card fraud. Studies suggest that machine learning techniques can help overcome the challenges of identifying fraudulent transactions with high detection rates, both directly and indirectly. While supervised ML algorithm and unsupervised ML algorithms have been proposed, the limitations of each approach underscore the need for hybrid methods. From the results, it is evident that Logistic Regression has the highest accuracy of 94.86%, highest recall value of 98.97% and highest F1-score of 95.09%. © 2023 IEEE.

关键词： Feature extraction

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Research on the application of image-based analysis in interaction design 4

Research on the application of image-based analysis in inter...

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4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Yu, Ying Zhang, Mei Shenyang jianzhu university Institute of Design and Art Hunnan Liaoning Shenyang China

ISBN: (纸本)9781510674448

Under the rapid development of today's society, our life is more and more inseparable from intelligence. At present, through the widespread popularization of intelligent technology and the gradual deepening of research projects, image analysis technology has been applied in many fields, and for image analysis technology to be applied to the field of interaction design can further enhance the sense of intuitive and intelligent user experience, and can also provide a strong and powerful support for interaction design. this paper provides a comprehensive analysis of the research field of image analysis and the application of interaction design, and identifies the advantages of the combination of the two, as well as makes further optimization for the shortcomings. © 2024 SPIE.

关键词： Image analysis

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