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检索条件"任意字段=6th Asia Conference on Machine Learning and Computing, ACMLC 2024"

共 606 条记录，以下是41-50 订阅

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IoT Based Accident Prevention System using machine learning techniques 6

IoT Based Accident Prevention System using Machine Learning ...

引用

6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th asia Digital Image Processing conference

作者： Alnashwan, Raghad Mashaabi, Malak Alotaibi, Areej Qudaih, Hala Albraheem, Lamya King Saud Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh Saudi Arabia

ISBN: (纸本)9798400716225

the likelihood of car accidents increases during extreme weather conditions, such as fog, winds, snow, rain, etc. While it may not be possible to prevent all such accidents, their incidence can be reduced by taking proper measures. therefore, an intelligent accident-avoidance system is necessary to predict the severity of accidents based on weather and road conditions. this research paper suggests three machine learning (ML) methods for an Internet of things (IoT)-based accident severity prediction system. the methods are Random Forest, LightGBM, and *** aim is to predict the severity of car accidents based on various weather features using a machine learning model. However, considering the previous work, we observed that the size of datasets is frequently minimal, and some of the research discusses the influence of the weather on the number of accidents. therefore, we used the Countrywide Traffic Accident Dataset, which covers 2.8 million vehicle accidents in the United States from 2016 to 2021. In conclusion, our methodology appears to be efficient in predicting the severity of car accidents. Among the three methods, LightGBM achieved the highest prediction accuracy (72%), precision (70%), recall (70%), F1-scores (70%), and area curve (AUC) (0.86) of the receiver operating characteristic (ROC) curve.

关键词： IoT machine learning LightGBM Vehicle

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HeteroEML: Heterogeneous Design Methodology of Edge machine learning on CPU plus FPGA Platform 6

HeteroEML: Heterogeneous Design Methodology of Edge Machine ...

引用

6th International conference on AI Circuits and Systems (AICAS)

作者： Wu, Yi-Ting Yen, Tzu-Yun Lin, Yu-Pei Lai, Bo-Cheng Natl Yang Ming Chiao Tung Univ Inst Elect Hsinchu Taiwan

ISBN: (纸本)9798350383638;9798350383645

the diverse applications with a wide variety of machine learning (ML) models have made fast design and deployment of ML computing systems an imperative task. the integration of CPU and FPGA have become a suitable ML computing platform to concurrently support programmability on CPU as well as high performance processing on the logic of FPGA. However, deploying ML models on CPU+FPGA platforms is challenging due to increasing model complexity and the need for cross-layer optimization. this paper proposes HeteroML, a heterogeneous design methodology of edge ML on CPU+FPGA platforms. We developed a customized end-to-end compilation process of ML models. the proposed methodology is based on TVM compilation framework, and enables seamless SW/HW integration and fast and effective optimization flow. When compared to conventional CPU-based edge systems, the proposed design can attain 13.78x and 6.47x performance enhancement on VGG and YOLOv2 respectively.

关键词： Heterogeneous integration SW/HW co-design machine learning compiler accelerator

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Cognitive computing in English Language Classrooms 6

Cognitive Computing in English Language Classrooms

引用

6th International conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2024

作者： Shaik, Iliyas Ali Shaik, Ansar Hussain Athahar, P. Rasheeda Begum, B. The Apollo University School of Technology Chittoor India Annamacharya University Department of Humanities & Sciences Rajampeta India Madanapalle Institute of Technology and Sciences Department of English & Foreign Languages Madanapalle India Ksrmce Department of Humanities and Science Andhra Pradesh Kadapa India

ISBN: (纸本)9798331505790

A paradigm shift has occurred in the field of English language instruction with the advent of cognitive and machine learning technologies. this study delves into how cognitive science and machine learning come together to create cutting-edge software and language-learning tools for English language courses. It explores the theoretical underpinnings of language learning cognition, surveys the state-of-the-art machine learning tools used in language education, and then looks at how these tools are put to use in language classrooms and on language learning software. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Chatbot User Satisfaction: A machine learning Approach Integrating Decision Tree, TF-IDF, and BERTopic 6

Enhancing Chatbot User Satisfaction: A Machine Learning Appr...

引用

6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Lu, Jiaxin Trine University Phoenix United States

ISBN: (纸本)9798350374315

Ensuring that large language models (LLMs) generate responses that resonate with users is critical for successful interactions. this study leverages machine learning techniques to improve chatbot interactions by aligning responses more closely with human preferences. By analyzing the Chatbot Arena dialogue dataset, we identified key features influencing user satisfaction and employed an ensemble model combining Decision Tree, TF-IDF, and BERTopic to enhance prediction accuracy. Our results demonstrate significant improvements in both accuracy and user satisfaction metrics compared to traditional models. this approach addresses the complexities of human-computer dialogue, providing a robust framework for future enhancements. the integration of advanced feature engineering and topic modeling techniques enables the chatbot to generate more contextually relevant and engaging responses, setting a new standard in the field of conversational AI. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Network Personalized Recommendation Algorithm Based on machine learning 6

Research on Network Personalized Recommendation Algorithm Ba...

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6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Yu, Jiang Guangzhou Huali College Guangzhou City China

ISBN: (纸本)9798350374315

As a tool of information dissemination, Internet not only provides abundant information, but also brings serious information overload. Recommendation algorithm can extract interests and preferences from users' historical information, so as to make personalized information recommendation to users, which has been widely used in various scenarios in the Internet field. With the development of artificial intelligence, especially the application of machine learning, recommendation algorithm has developed into a multidisciplinary research direction of integrating neural networks, knowledge graphs and natural language processing. In order to alleviate the problem of 'information cocoon' caused by blind pursuit of recommendation accuracy, this paper mainly proposes a fuzzy algorithm recommendation model with dynamic threshold based on machine learning model. By improving association rules, the data imbalance in the data set is adjusted, the speed of data processing is improved, and the dynamic threshold adjustment strategy is adopted to provide personalized information services to users accurately. © 2024 IEEE.

关键词： Adversarial machine learning

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A Systematic analysis on Security and Anomaly Detection using machine learning in Cloud computing 6

A Systematic analysis on Security and Anomaly Detection usin...

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6th International conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2024

作者： Tanam, Aparna Raja, G. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9798331505790

the term 'cloud computing' describes a way of using the Internet to access resources, software, and databases without being constrained by local hardware. Businesses adopting this technology can grow their operations more flexibly by outsourcing all or a portion of the management of their infrastructure to outside hosting companies. Cloud-based security services are increasingly being used by contemporary enterprises. the dynamic and unpredictable environment, particularly in online applications and apps for the general public, can provide a number of issues even when organizations fully resource their departments. these as-a-service business models let companies outsource a variety of time-consuming IT-related jobs. the traditional intended approaches have significant difficulties with data loss prevention, anomaly detection, and data analysis. Moreover, the current cloud computing system has shortcomings in the areas of causal effect analytics, anomaly detection, and data loss prevention. the machine learning technique greatly increases the effectiveness of anomaly detection in a cloud computing network. this research effort introduces a novel approach to deal with the system's constraints in its current state. the suggested work introduces proactive security mechanisms to stop the loss of sensitive data. A signature system based on machine learning is utilised to detect anomalies. © 2024 IEEE.

关键词： Cloud security

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Evaluating machine learning Techniques for Detecting learning Difficulties: A Focus on Moroccan Baccalaureate Students 6

Evaluating Machine Learning Techniques for Detecting Learnin...

引用

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

作者： Ouhaddou, Chaimae Retbi, Asmaa Bennani, Samir MOHAMMED v UNIVERSITY RIME TEAM-Networking Modeling and E-Learning Team-Masi Laboratory-Engineering.3S Research Center RABAT Morocco

ISBN: (纸本)9798350351200

In recent years, researchers have begun exploring how machine-learning techniques can be applied to education, particularly in the identification of students with learning difficulties. this article aims to determine the most accurate and precise machine learning technique for identifying learning difficulties among second year baccalaureate students way before the final examination. Consequently, three academic courses are selected, each forming a separate dataset, and three classification algorithms are tested. the selected models aims to determine the necessity of supplementary courses for students at risk, by offering tailored recommendations on whether and when additional courses may be beneficial. the study's findings showed that, in comparison with other techniques, Decision Tree algorithm achieved the best outcomes. © 2024 IEEE.

关键词： Contrastive learning

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

6th International Conference on Intelligent Computing in Dat...

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

ISBN: (纸本)9798350351200

the proceedings contain 115 papers. the topics discussed include: MEDIFICS: model calling enhanced VLM for medical VQA;N-gram based HASSANIYA dialect classification;a comprehensive review on machine learning advancements for plant disease detection and classification;utilizing deep learning and machine learning methods to forecast market performance;optimizing video compression quality using AI-boosted HEVC;performance of prophet in stock-price forecasting: comparison with ARIMA and MLP networks;abstractive biomedical long document summarization through zero-shot prompting;comprehensive and comparative analysis between transfer learning and custom built VGG and CNN-SVM models for wildfire detection;and resume ranker: AI-based skill analysis and skill matching system.

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Mining Haulage Road Maintenance Prediction Dashboard Utilising machine learning Techniques 6

Mining Haulage Road Maintenance Prediction Dashboard Utilisi...

引用

6th International conference on Advancements in computing, ICAC 2024

作者： Purdon, Brad Piyathilaka, Lasitha Sul, Jung-Hoon Preethichandra, Daluwathu Central Queensland University School of Engineering and Technology Rockhampton Australia

ISBN: (纸本)9798331517878

this paper details the development of a predictive maintenance dashboard designed for haulage roads in surface mining operations using machine learning algorithms. We assessed various machine learning models, including Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN), to determine their effectiveness in predicting the timing and location of necessary road maintenance. the research identifies the Logistic Regression model as the most effective, which was trained on data including vibration measurements, equipment counts, and speed metrics to evaluate road surface conditions. these inputs are processed through a logistic function to estimate the probability of required repairs. the predictions generated by the model are stored in an SQL database and visualized through Power BI, which offers a user-friendly interface. this interface enables operators, including those with limited experience, to interpret the data effectively and make informed maintenance decisions, thus improving operational efficiency and safety. © 2024 IEEE.

关键词： Predictive maintenance

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the Research on Fault Diagnosis of Electric Power Equipment Using machine learning Technology 6

The Research on Fault Diagnosis of Electric Power Equipment ...

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6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Gao, Huan Pu, Baoming Liaoning Institute of Science and Engineering Jinzhou China

ISBN: (纸本)9798350374315

this paper proposes a new Antennae Search Recurrent Unit (ASRU) model, which combines Gated Recurrent Unit (GRU) neural network with Beetle Antennae Search algorithm. It is applied to the fault diagnosis of power equipment. the ASRU model inherits the advantages of GRU neural network, and introduces the randomness of the scour algorithm to improve the search ability and robustness of the model. In this paper, the application effect of ASRU model in power equipment fault diagnosis is verified by experiments. the outcomes from the experimentation indicate that the ASRU model boasts heightened accuracy and sensitivity when diagnosing various kinds of power equipment malfunctions, thereby showcasing clear superiority over alternative cutting-edge models. © 2024 IEEE.

关键词： fault detection GRU neural networks machine learning

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