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检索条件"任意字段=8th International Conference on Data Science and Machine Learning Applications, CDMA 2025"

共 693 条记录，以下是81-90 订阅

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Sentiment Analysis on Moroccan Dialect of Arabic Combining NLP and ML Methods 8th

Sentiment Analysis on Moroccan Dialect of Arabic Combining N...

引用

8th international conference on Arabic Language Processing

作者： Ladrham, Khalil Gueddah, Hicham Mohammed V Univ Intelligent Proc & Secur Syst Fac Sci BP 8007Ave Nations Unies Rabat Morocco ENS Mohammed V Univ Intelligent Proc & Secur Syst Team BP 8007Ave Nations Unies Rabat Morocco

ISBN: (纸本)9783031791635;9783031791642

In recent years, machine learning and websites have developed rapidly. this resulted in continual and explosive growth in the sharing of ideas and views on products and services over the worldwide web in an array of sectors. As a result, there is an enormous flow of internet data attainable for analytical research. Sentiment analysis (SA) is a part of Natural Language Processing (NLP) that requires to process enormous amounts of data in order to identify people's opinions and sentiments. Several studies have been conducted to deal with the negative effects of social networks. this field of research is increasing popularity in both the public and private sectors, leading to the creation of several challenges. However, the majority of the available datasets were in English. Whereas the Arabic Moroccan dialect (Darija) ones were not. Following that, we created models combining NLP and Marching learning techniques to detect and classify sentiments. We evaluated the models using the most used metrics: accuracy, loss, F1-score, precision, and recall. the results of the experiment revealed modest scores between 87% and 89%. these findings imply that the models require to be upgraded due to a lack of accessible datasets and pre-processing techniques to handle the Moroccan dialect of Arabic (Darija).

关键词： Sentiment analysis Arabic Moroccan dialect Natural Language Processing NLP Maching learning ML pre-processing datasets

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Cloud-Based Emotion Recognition Application for Arabic Autistic Kids

Cloud-Based Emotion Recognition Application for Arabic Autis...

引用

conference on data science and machine learning applications (cdma)

作者： Abeer Al-Qahtani Fatmah Al-Mahawes Fatmah Al-Qahtani Lamia Berriche Souad Larabi-Marie Sainte Computer Science Department Imam Mohammad Ibn Saud Islamic University Riyadh KSA Computer Science Department Prince Sultan University Riyadh KSA

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Recognition of emotional facial expressions is an early developing social skill. Strengthening social skills improves relationships between people, success, and happiness. People with autism suffer from an inability to recognize their emotions and others' emotions. Emotion recognition applications for autistics could help children suffering from autism to better integrate into society by providing a new way to understand their feelings and others. In this work, we proposed a cloud-based facial expression recognition mobile application for Arabic autistic kids. We used SkyBiometry cloud-based API for face detection and emotion recognition features. the application, My emotion…My Communication includes a self-emotion recognition feature as well as a matching game helping the kid recognize others' emotions.

关键词： Emotion recognition Autism Face recognition Games machine learning Mobile applications Multiaccess communication Usability Monitoring Smart phones

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Partial Distributed Deep learning Inference Model for Image based Edge Device Cluster 24

Partial Distributed Deep Learning Inference Model for Image ...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Xiao, Dun Wang, Xiaogang Yang, Zhihe Huang, Chao School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being deployed in smart manufacturing edge application scenarios to carry out edge detection tasks. However, there is a gap between the expensive DNN computing network and the limited computing power of the edge devices. To deploy compute-intensive neural networks on resource-constrained edge devices, traditional approaches rely either on offloading workloads to a remote central cloud or on optimizing compute on local edge devices. However, the method of uploading tasks to the central cloud is significantly affected by the unreliability and delay of the WAN, and the sensitive information of users is easy to be leaked during transmission, and the local computing method is also limited by the computing power. the latest machine learning models have great demands on memory, computing, and energy, which poses challenges to distributed computing and data aggregation for heterogeneous and resource-constrained edge devices. In order to address these challenges, this paper proposes solutions for different image types in intelligent manufacturing scenarios. When images are processed in the production process of intelligent manufacturing, this paper adopts the method of horizontal partitioning of the DNN model to perform DNN-based inference tasks on the edge device cluster. In order to reduce the consumption of network bandwidth, a dynamic priority scheduling algorithm is used to optimize the distribution of inference tasks at the edge nodes to improve the inference speed. Finally, experiments show that the edge node optimization scheduling method proposed in this paper has a good effect on the performance of running time, communication cost and energy consumption of edge devices. © 2024 Copyright held by the owner/author(s).

关键词： Smart manufacturing

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An Intelligent Feature Extraction and Multiple learning-Based Classification Techniques for Diagnosis of Breast Cancer 8th

An Intelligent Feature Extraction and Multiple Learning-Base...

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8th international conference on Internet of things and Connected Technologies, ICIoTCT 2023

作者： Patro, Satyabrata Mishra, Jyotirmaya Panda, Bhavani Sankar Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam531162 India

ISBN: (纸本)9789819757855

Breast cancer is often curable if discovered in its early stages. machine learning algorithms are used to automate disease identification. To increase the likelihood of recognizing the condition at an early stage, an effective classifier for automated diagnosis of breast cancer is needed. this research attempts to introduce a mixed machine learning model for predicting breast cancer. To outperform other strategies, it requires enormous amounts of data. Moreover, the intricate data models increase the training cost. For enhancing the efficiency of basic classifiers, ensemble learning holds great promise. A rapid discrete wavelet transforms using the wrapping approach is then used to extract the characteristics from the Region of Interest (ROI) mammography pictures. In addition to being too high to be categorized, the extracted wavelet coefficients also have incredibly high run-time complexity. the modified whale optimization method, which employs swarm intelligence, has been presented to lower time complexity and select the significant characteristics. Ensemble learning has been applied for the requisite efficiency in the suggested technique. three machine learning (ML) classifiers—the extreme gradient boost classifier (XGB), support vector machine (SVM), and enhanced granular neural network (E-GNN)—form the ensemble voting system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Computational Analysis of Quran Text Using machine learning and Large Language Models

Computational Analysis of Quran Text Using Machine Learning ...

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conference on data science and machine learning applications (cdma)

作者： Usama Shahid Muhammad Zunnurain Hussain William Sayers School of Business Computing and Social Sciences University of Gloucestershire Cheltenham United Kingdom Department of Computer Science Bahria University Lahore Pakistan

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

the Quran verses are foundational for Muslims worldwide. Significant research has been dedicated to information retrieval (IR) from Quran; however, multiple studies have focused on descriptive analysis and topic modelling of the Quran in Arabic and translated versions. this study presents a comprehensive framework for analysing large textual data using an English translation of the Quran. Initially, it conducts a descriptive analysis of the verses to uncover various features, including readability, word clouds, significant n-grams, and network graphs illustrating word associations. the framework then applies machine learning techniques, specifically clustering models based on numerical vectors from text-embedding-3-large, to identify effective groupings of verses. Additionally, GPT-4-turbo is used for topic modelling within each cluster through prompt engineering, aiming to enhance the understanding of these clusters. the results include statistical information graphs and concise knowledge summaries that are beneficial to both domain experts and wider populace.

关键词： Translation Semantic search machine learning Tag clouds Vectors Natural language processing Numerical models data mining Prompt engineering Multiaccess communication

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Research on Recommendation Algorithm Based on TrustSVD++ and Deep Matrix Decomposition 24

Research on Recommendation Algorithm Based on TrustSVD++ and...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Chen, Gongjin Kong, Yuting He, Yantao Department of Computer Science Guangdong University of Science and Technology Guangdong Dongguan China

ISBN: (纸本)9798400710094

In the recommendation algorithm, due to the incompleteness of the interaction between users and items, there are often a large number of missing data in the interaction matrix, which affects the quality of recommendation. To address this challenge, researchers have introduced matrix factorization techniques combined with machine learning algorithms to fill in missing data. However, the bilinear mapping technology adopted by the traditional matrix factorization method cannot fully capture the nonlinear relationship between users and items. To this end, this study proposes a recommendation algorithm combining TrustSVD++ and deep matrix factorization. Firstly, the explicit and implicit trust information between users is integrated into the SVD++ model to fill the interaction matrix more accurately. then, in order to preserve the linear relationship and prevent network degradation, the residual network was used as the core module, and the parameter adjustment was optimized by adding channels directly connecting the input and output. Experimental results on public datasets show that compared with DMF1, DMF2, MC+DMF and BDMF models, the proposed TDMF model has the hit rate (HR@10) and normalized discounted cumulative gain (NDCG@10) increased by 2.1%and 3.7%respectively, which proves the effectiveness and practicability of the proposed model. © 2024 Copyright held by the owner/author(s).

关键词： Matrix factorization

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Emotion Recognition in Virtual Reality learning Environments: A Multimodal machine learning Approach 17th

Emotion Recognition in Virtual Reality Learning Environments...

引用

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

作者： Romero-Ramos, Luis González-Serna, Gabriel López-Sánchez, Máximo González-Franco, Nimrod Valenzuela-Robles, Blanca Computer Science Department TecNM/CENIDET Morelos Cuernavaca62490 Mexico

ISBN: (纸本)9783031838811

Positive emotions significantly impact learning outcomes as they foster problem-solving and decision-making, while negative emotions can hinder information processing. Traditional methods for detecting emotions in immersive virtual reality (VR) applications, such as electroencephalography (EEG) and electrocardiography (ECG), are often invasive, expensive, and susceptible to noise. this paper introduces a new, non-invasive approach for classifying emotional valence in Virtual Immersive learning Environments (VILE) based on analyzing head and hand movements using machine learning algorithms. A modified Self-Assessment Mannequin collected multimodal behavioral data from 30 students. the results show promising accuracy (71%) in distinguishing between positive and negative emotional states. While this method currently focuses on valence classification rather than specific emotion recognition, it paves the way for developing more accessible and less intrusive solutions for emotional detection in VR learning contexts. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Virtual environments

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Confluence of machine learning and Internet of things for E-Healthcare Security 8th

Confluence of Machine Learning and Internet of Things for E-...

引用

8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Pattnayak, Parthasarathi Das, Tulip Mohanty, Arpeeta Patnaik, Sanghamitra School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India Faculty of Emerging Technologies SRI SRI University Odisha Cuttack India School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India School of Law KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789819784219

In the current era, the e-communication technology is developing swiftly and has made a remarkable transformation in e-healthcare sector by facilitating efficient and secure transmission of patient’s data. Internet of things (IoT) is developing drastically in the e-healthcare sector and the intelligent devices are also improving with many new innovative enhancements. the confluence of IoT and ML is manifested in many sectors but its implementation in e-healthcare sector provides real-time surveillance and monitoring of patient’s data and health status through prediction, anticipation and the capability of taking decisions intelligently. Nowadays, cryptographic and biometric systems, anomaly detection and machine learning (ML) approaches for authentication are being widely used for ensuring the security and functioning of healthcare systems. the present study aims to know how the convergence of IoT e-healthcare management system (EHMS) and ML techniques helps to design automated systems for proper monitoring and intelligent decision making. It also focuses to present an innovative architecture to safeguard the real-time healthcare from outside threats with the consumption of less possible resources of low-powered devices. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Utilizing Pretrained Vision Transformers and Large Language Models for Epileptic Seizure Prediction

Utilizing Pretrained Vision Transformers and Large Language ...

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conference on data science and machine learning applications (cdma)

作者： Paras Parani Umair Mohammad Fahad Saeed Knight Foundation School of Computing and Information Sciences Florida International University Miami FL USA

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Repeated unprovoked seizures are a major source of concern for people with epilepsy. Predicting seizures before they occur is of interest to both machine-learning scientists as well as clinicians, and is an active area of research. the variability of EEG sensors, type of seizures, and specialized knowledge required for annotating the data complicates the large-scale annotation process essential for supervised predictive models. To address these challenges, we propose the use of Vision Transformers (ViTs) and Large Language Models (LLMs) that were originally trained on publicly available image or text data. Our work leverages these pre-trained models by refining the input, embedding, and classification layers in a minimalistic fashion to predict seizures. Our results demonstrate that LLMs outperforms the ViTs in patient-independent seizure prediction achieving a sensitivity of 79.02% which is 8% higher compared to ViTs and about 12% higher compared to a custom-designed ResNet-based model. Our work demonstrates the successful feasibility of pre-trained models for seizure prediction with its potential for improving the quality of life of people with epilepsy. Our code and related materials are available open-source at: https://***/pcdslab/UtilLLM_EPS/

关键词： Computer vision Sensitivity Large language models Refining Epilepsy machine learning Predictive models Brain modeling Transformers Sensors

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Towards Automatic Arabic Test Scoring: Leveraging ChatGPT for Prompt Design

Towards Automatic Arabic Test Scoring: Leveraging ChatGPT fo...

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conference on data science and machine learning applications (cdma)

作者： Seham Alsulami Amal Alsubhi Abrar Alabdli Tarik Alafif Mohammad Jassas Khaled Albishre Computer Science Department Jamoum University College Umm Al-Qura University Jamoum Saudi Arabia

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

ChatGPT possesses powerful large language models that rely on deep learning technology. It excels at understanding natural language and context, enabling it to generate answers to questions and inquiries. However, evaluating and grading high school Arabic Achievement tests in Saudi Arabia requires human interventions by providing correct test answers into the tests platforms. In this paper, we propose a web-based system (AutoGrader), which includes a framework that automatically grades the students' tests without human interventions. the framework relies on the ChatGPT and Term Frequency-inverse Document Frequency (TF-IDF) algorithm. the framework compares students' answers with those generated by the ChatGPT using the TF-IDF similarity measure and then displays students' grades rapidly. the framework is evaluated using students' tests. the framework is efficient and effective, which can be used to improve and enhance educational assessments and experiences.

关键词： Deep learning Large language models Natural languages data science Chatbots Multiaccess communication

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