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machine learning-Based Prediction of MPI Communication Time in High-Performance computing Applications 6

Machine Learning-Based Prediction of MPI Communication Time ...

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6th International conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Wang, Zhuang Zhuang, Yuan Zeng, Yunhui Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

ISBN: (纸本)9798331541750

the rapid advancements in high-performance computing (HPC) have made large-scale parallel computing feasible. As a commonly used parallel programming model, Message Passing Interface (MPI) plays a crucial role in HPC systems and directly affects the performance and efficiency of these applications. therefore, modeling and predicting the performance of MPI communication is critically important. this paper utilizes a machine learning-based approach to predict MPI communication time. It uses performance analysis tools to collect real-time communication data and select critical features related to execution time. the data is categorized, then, for each type of MPI communication, a dedicated regression model is trained. Firstly, XGBoost and Artificial Neural Networks (ANN) are employed for function level modeling. then, fusion techniques are used to combine XGBoost and ANN models to improve prediction accuracy. Finally, these function level models are integrated into an application level model to predict the overall communication time. this model is evaluated using CG, FT, and CPMD. Experimental results show that the average prediction errors of the model are 12.6%, 13.2%, and 13.5%, with a minimum error of 7%. © 2024 IEEE.

关键词： Prediction models

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(Voick): Enhancing Accessibility in Audiobooks through Voice Cloning Technology 6

(Voick): Enhancing Accessibility in Audiobooks through Voice...

引用

6th International conference on computing and Informatics, ICCI 2024

作者： Mahmoud Ahmed, Mahmoud Ahmed Elghamrawy, Karim Ahmed Abd El Haliem Taha, Zeinab Egypt

ISBN: (纸本)9798350373875

Voice cloning, a transformative facet of text-to-speech synthesis, sits at the confluence of linguistics, machine learning, and audio processing. this research delves into its applications, challenges, and ethical dimensions within the context of audiobook production. Current systems often struggle to replicate the nuances of human speech, impacting user immersion. this paper objectives encompass evaluating the efficiency and quality of voice cloning, exploring customization capabilities and overcoming technical challenges. the motivation stems from the resurgence of the audiobook industry, a demand for high-quality content, the efficiency and customization potential of voice cloning, and its role in enhancing accessibility. By investigating these facts, this research aims to empower audiobook narrators, content creators, and publishers with insights to elevate the industry's quality and accessibility through advanced voice cloning technology. the paper examines the 3 models that make up the system, and analyze results generated by their implementation with data. © 2024 IEEE.

关键词： learning algorithms

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machine learning-Driven Toolkit for Sinhala Text-to-Speech and Spell Checker 6

Machine Learning-Driven Toolkit for Sinhala Text-to-Speech a...

引用

6th International conference on Advancements in computing, ICAC 2024

作者： Praveen, Navindu Fernando, Harinda Gamage, Manori Sri Lanka Institute of Information Technology Faculty of Computing Malabe10115 Sri Lanka

ISBN: (纸本)9798331517878

this research introduces a web-based education system to assist Sri Lankan Grade 5 students overcome challenges in learning the Sinhala language. Mastering Sinhala is essential for success in the Grade 5 Scholarship Examination, where students often need help with complex grammar, syntax, and vocabulary. these challenges can significantly hinder their performance in this crucial exam. the toolkit uses advanced NLP and ML technologies to improve academic spelling and intonation during reading. Key contributions include the development of a Sinhala Spell Checker, implementing Random Forest algorithms with an 82% accuracy rate, and a text-to-speech (TTS) system built on a custom neural network achieving 98% accuracy. the research employs a user-centered design approach, creating a unique dataset of over 100,000 words to train and enhance the Random Forest algorithm. Evaluation of Grade 5 students indicated that more than 90% reported improved language acquisition, marking the toolkit's effectiveness. By integrating these technologies, the project aims to address educational gaps, providing significant assistance for learners. Future research will further enhance the toolkit's accuracy and expand its application in language learning. © 2024 IEEE.

关键词： Random forests

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On the Robustness of Deep Face Inpainting: An Adversarial Perspective 6

On the Robustness of Deep Face Inpainting: An Adversarial Pe...

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6th ACM International conference on Multimedia in asia, MMasia 2024

作者： Gao, Wenhao Song, Zhenbo Zhang, Zhenyuan Lu, Jianfeng Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400712739

Recent developments in deep learning-based face inpainting models have shown significant progress in their performance on standard datasets. However, the robustness of these models against adversarial attacks has not been thoroughly examined, which is critical for their practical use. To address this research gap, this paper evaluates the robustness and reliability of various face inpainting models using the PGD adversarial attack method. the evaluation includes adding perturbations to both the entire image region and the five facial landmark regions. the results reveal that models with superior performance on standard datasets are not necessarily more robust. Furthermore, models incorporating prior information about the face tend to be more robust. Additionally, we evaluated the impact of attacking face inpainting models on the performance of downstream face tasks such as face parsing and facial landmark detection. the findings offer insights for designing better face inpainting models in the future, with improved reliability and security for real-world applications. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Identification of Nutrient Deficiency Based on Leaf Image Data Using machine learning 6

Identification of Nutrient Deficiency Based on Leaf Image Da...

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6th IEEE International conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Kavitha, S. School of Computer Engineering MIT Academy of Engineering Pune India

ISBN: (纸本)9798350306613

the world population has been growing significantly during the last few decades. the demand for rice is constantly increasing with the ever-increasing global population, as rice crops are one of the primary food sources for humans. Increased fertilizer use in recent years has helped farmers to raise their rice production rapidly. Adding too much of fertilizers will have a negative effect on the soil. To boost the rice production, it is necessary to ensure that each paddy plant is healthy. this study makes use of a very practical method to identify nutrient deficiencies (NPK) in rice plants, using leaf imaging and machine learning techniques. the study proposes a framework in which the color and texture features of the image are extracted and then combined to get a single robust feature representation of the image. Classification of the image is done for identifying macro nutrient deficiencies using the Random Forest model, which when compared with Naive Bayes gave better accuracy. © 2024 IEEE.

关键词： machine learning Naïve Bayes NPK Nutrient deficiency Random Forest

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the Effect of Adversarial machine learning Attack on Agriculture Field and Food Security 6

The Effect of Adversarial Machine Learning Attack on Agricul...

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6th International conference on Intelligent computing in Data Sciences, ICDS 2024

作者： Alferidah, Dhuha Khalid Algosaibi, Abdulelah Department of Computer Science Alahssa Saudi Arabia

ISBN: (纸本)9798350351200

machine learning and Deep learning models had enabled several smart applications such as classification and recognition of objects. Smart machine and deep learning models can provide effective solutions to real life problems in various sectors such as agricultural and food security. Agriculture plays a significant role in food security where it supplies nations with the needed nutritious. Food security matters for nations as it ensures continuity and sufficiency of food and nutritious for humans. machine and Deep learning models are exposed to a cybersecurity attack that is called the adversarial machine learning attack. this attack employes different methods and different algorithms to hamper the performance of the models. the attack initiates a noise attack over the images, videos, audios, text data that is being processed by the model. this results in the model misfunctioning and misclassifying the data which reduces the number of correctly classified samples and increase the number of wrongly classified samples by the models. the interesting aspect of the attack is that the human eye cannot detect the change of the data such as images. the data/images look identical before and after the adversarial attack. Only the models can detect the change in pixels and therefore the performance of the model degrades. this research paper provides a comprehensive literature review over the most common and widely used algorithms of the adversarial attack. the paper explores into the critical issue of food security, shedding light on its significance in global contexts. It highlights the potential consequences of adversarial attacks, emphasizing their unfavorable impact on food security and production systems. By drawing attention to these challenges, the paper calls for urgent measures to safeguard food security infrastructure against emerging cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Endotracheal Suctioning Classification: Leveraging Prompt Engineering in machine learning for Feature Selection

Optimizing Endotracheal Suctioning Classification: Leveragin...

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6th International conference on Activity and Behavior computing (ABC)

作者： Islam, Mahera Roksana Ferdous, Anik Mahmud Hossain, Shahera Ahad, Md Atiqur Rahman Alnajjar, Fady Univ Dhaka Dept Elect & Elect Engn Dhaka Bangladesh Univ East London Dept Comp Sci & Digital Technol London England UAE Univ Coll Informat Technol Dept Comp Sci & Software Engn Al Ain U Arab Emirates

ISBN: (纸本)9798350375510;9798350375503

In a world with an overgrowing elderly population, there exists a critical need for a greater number of skilled individuals in the nursing industry. AI-based systems can be useful, compared to traditional ones with require in-person assistance, to accurately identify nursing activities and assess the nursing trainees to help them become proficient. this paper addresses classifying activities in one such procedure, endotracheal suctioning, using skeletal keypoint data of the subject performing the procedure. A multi-step structured prompt engineering method was established and utilized on several LLMs to select or calculate key features from the data. then the features are passed onto several tuned machine learning models to obtain results. A tuned XGBoost prevailed across all models, achieving 90% accuracy on the validation set.

关键词： Human Activity Recognition Large Language Model Generative AI machine learning Nurse-care

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Fine-Tuned Large Language Model for Autonomous Vehicles Accident Report 6

Fine-Tuned Large Language Model for Autonomous Vehicles Acci...

引用

6th International conference on machine learning, Big Data and Business Intelligence, MLBDBI 2024

作者： Feng, Yao Huang, Xingyu Zhang, Zhixiang School of Computer Science and Technology University of Wollongong SydneyNSW Australia

ISBN: (纸本)9798331541798

In recent years, with the rapid development of large language models, the application of artificial intelligence in various fields has made remarkable progress, especially in the research and development and application of autonomous driving technology, this trend is particularly obvious. In this study, Low-Rank Adaptation (LoRA) technology and BitsAndBytes quantization technology were used to fine-tune LLaMA-2 7B model efficiently. the introduction of these techniques is designed to reduce the dependence on computing and memory resources during model fine-tuning, making it possible to effectively optimize large pretrained models even in resource-limited environments. the experimental results show that our fine-tuned model achieves the best effect in the text generation task, with the overall cosine similarity reaching 0.740 and Jaccard similarity reaching 0.620, which can accurately perform the text generation task, proving the effectiveness of the fine-tuned model. It also provides a new idea for applying large language model to autonomous driving accident analysis in the future. © 2024 IEEE.

关键词： Deep learning

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Decoding Diabetics: Diabetic Retinopathy Detection using machine learning 6

Decoding Diabetics: Diabetic Retinopathy Detection using Mac...

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6th International conference on Advancements in computing, ICAC 2024

作者： Arunthavarajah, Venuganth Tissera, Wishalya Rajendran, Karthiga Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

Diabetic Retinopathy, a serious complication linked with diabetes, involves the progressive damage of retinal blood vessels and can lead to severe vision loss or blindness if not detected and treated promptly. this research paper explores the application of machine learning (ML) techniques for diagnosing and detecting Diabetic Retinopathy. the study examines essential components including data gathering, preprocessing, model training, and evaluation, highlighting the critical role of early detection and timely treatment in preventing severe visual impairments. Unlike traditional screening methods that depend on manual evaluation by ophthalmologists, which can be labor-intensive and limited in remote or underserved regions, ML algorithms offer a scalable and efficient solution for Diabetic Retinopathy screening through retinal image analysis. this research demonstrates the effectiveness of ML models in automating the detection process. By employing advanced preprocessing techniques like data augmentation and using sophisticated Convolutional Neural Network (CNN) architectures, the proposed method achieves high accuracy and reliability in identifying various stages of Diabetic Retinopathy. the integration of these technologies showcases the potential of ML to revolutionize Diabetic Retinopathy screening practices, enhancing accessibility and improving patient care. the findings of this study highlight the capacity of ML to significantly advance Diabetic Retinopathy detection and management, offering a more efficient and automated approach that could greatly benefit individuals at risk of this condition. © 2024 IEEE.

关键词： Convolutional neural networks

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machine learning and Browser Fingerprinting Based Approach for Web Bot Detection 6

Machine Learning and Browser Fingerprinting Based Approach f...

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6th International conference on Advancements in computing, ICAC 2024

作者： Pathirage, Gihan Saminda Manathunga, Kalpani Sri Lanka Institute of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Web bots are becoming increasingly sophisticated, posing a serious threat to internet security and integrity. In response to this growing issue, this study proposes a novel bot detection method based on browser fingerprinting and machine learning. the proposed method is a model that was trained on browser fingerprints collected from web traffic using a custom script with a browser fingerprinting library. the traffic was classified as legitimate or malicious based on firewall rules and honeypots. this model seeks to enhance bot detection accuracy while decreasing false positives, filling a gap in the existing research on browser fingerprint-based bot detection. the experimental results demonstrate that detailed browser fingerprint data can serve as an effective indicator for distinguishing between human and automated traffic, providing a foundation for integrating browser fingerprinting into wider security measures. the study also analyzes the specific attributes on browser fingerprints that contribute most significantly to differentiating between legitimate and bot traffic, while acknowledging the limitations in the proposed method. © 2024 IEEE.

关键词： Bot (Internet)

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