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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Bo Dang Wenchao Zhao Yufeng Li Danqing Ma Qixuan Yu Elly Yijun Zhu Computer Science San Francisco Bay University Fremont CA US School of Software Engineering University of Science and Technology of China Hefei Anhui China School of Electronics and Computer Science University of Southampton Southampton UK Computer Science Stevens Institute of Technology Hoboken NJ US College of Computing Georgia Institute of Technology Atlanta GA US

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to-Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community.

关键词： Deep learning YOLO Computational modeling Visual impairment Medical services Real-time systems User experience

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An Intelligent Machine Learning Approach for Prediction of Road Accident Severity

An Intelligent Machine Learning Approach for Prediction of R...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Prashanth Ragam Namana Sri Rahul Pandi Balaji Sankati Jyothi Pavithra Danaveni School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Software Development Cell VIT-AP University Amaravati Andhra Pradesh India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety.

关键词： Support vector machines Radio frequency Road accidents Machine learning algorithms Predictive models Nearest neighbor methods Prediction algorithms Road safety Public security Random forests

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Research on Text Summarization Generator Method Based on Input Text Linguistic Features and Copy Mechanism

Research on Text Summarization Generator Method Based on Inp...

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Liuyang Wang Yangxin Yu Faculty of Computer & Software Engineering Huaiyin Institute of Technology of China Huai’an China

ISBN: (纸本)9781665405546

The Sequence-to-Sequence (Seq2Seq) of neural network (NN) method based on recurrent neural network (RNN) and attention mechanism plays an important role in information extraction and automatic summarization. However, this method can’t make full use of the text linguistic feature information, and there is a problem of unregistered words in the generated results, which affects the accuracy and readability of the text summarization. To solve this problem, the text linguistic feature is used to improve the input characteristics, and the copy mechanism is introduced to alleviate the problem of unregistered words in the summarization generation process. On this basis, a new method named Copy-Generator Model (CGM) based on Seq2Seq model is proposed to improve the effect of text summarization. The results of experiments using LCSTS (Large Scale Chinese Short Text Summarization) as the data source show that the proposed method in this paper can effectively improve the accuracy of summarization and can be applied to automatic text summarization.

关键词： Knowledge engineering Vocabulary Recurrent neural networks Soft sensors Semantics Artificial neural networks Linguistics

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Zihan Wang Siyang Song Cheng Luo Yuzhi Zhou Shiling Wu Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Computing and Mathematical Sciences University of Leicester

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5.

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A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

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computers, Materials & Continua 2022年第7期72卷 1123-1137页

作者： Xiaorui Zhang Jie Zhou Wei Sun Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044 China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The key to preventing the COVID-19 is to diagnose patients quickly and *** have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 ***,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 ***,many CNNs have complex structures and massive *** if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread *** solve above problems,this paper proposes a lightweight CNN classification model based on transfer *** the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing *** order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the *** study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by *** results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT *** to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU ***:***/ZhouJie-520/paper-codes.

关键词： Convolutional neural networks chest computed tomography image COVID-19 transfer learning mobileNetv2

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SolSearch: An LLM-Driven Framework for Efficient SAT-Solving Code Generation

SolSearch: An LLM-Driven Framework for Efficient SAT-Solving...

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IEEE/ACM International Conference on software engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)

作者： Junjie Sheng Yanqiu Lin Jiehao Wu Yanhong Huang Jianqi Shi Min Zhang Xiangfeng Wang School of Computer Science and Technology East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China School of Computer Science and Technology Shanghai Formal-Tech Information Technology Co. Lt East China Normal University Shanghai China

ISBN: (数字)9798331537111

ISBN: (纸本)9798331537128

The Satisfiability (SAT) problem is a core challenge with significant applications in software engineering, including automated testing, configuration management, and program verification. This paper presents SolSearch, a novel framework that harnesses large language models (LLMs) to discover and optimize SAT-solving strategies automatically. Leveraging a curriculum-based, trial-and-error process, SolSearch enables the LLM to iteratively modify and generate SAT solver code, thereby improving solving efficiency and performance. This automated SAT-solving paradigm has the advantage of being plug-and-play, allowing integration with any SAT solver and accelerating the development or design process of new SAT solvers (new methods). Our preliminary experimental results are encouraging by demonstrating that the LLM-powered paradigm improves state-of-the-art SAT solvers on general SAT benchmarks and significantly enhances the performance of the widely used Z3 solver (11% on PAR-2 score). These results highlight the potential for using LLM-driven methods to advance solver adaptability and effectiveness in real-world software engineering challenges. Future research directions are discussed to further refine and validate this approach, offering a promising avenue for integrating AI with traditional software engineering tasks.

关键词： Codes Large language models Configuration management Benchmark testing software engineering

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FD-LLM: LARGE LANGUAGE MODEL FOR FAULT DIAGNOSIS OF MACHINES

arXiv

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arXiv 2024年

作者： Qaid, Hamzah A.A.M. Li, Dan Zhang, Bo Ng, See-Kiong Li, Wei School of Software Engineering Sun Yat-Sen University Zhuhai China Institute of Data Science National University of Singapore Singapore Singapore School of Mechatronic Engineering China University of Mining and Technology Xuzhou China School of Computer Science and Technology China University of Mining and Technology Xuzhou China

Large language models (LLMs) are effective at capturing complex, valuable conceptual representations from textual data for a wide range of real-world applications. However, in fields like Intelligent Fault Diagnosis (IFD), incorporating additional sensor data—such as vibration signals, temperature readings, and operational metrics—is essential but it is challenging to capture such sensor data information within traditional text corpora. This study introduces a novel IFD approach by effectively adapting LLMs to numerical data inputs for identifying various machine faults from time-series sensor data. We propose FD-LLM, an LLM framework specifically designed for fault diagnosis by formulating the training of the LLM as a multi-class classification problem. We explore two methods for encoding vibration signals: the first method uses a string-based tokenization technique to encode vibration signals into text representations, while the second extracts statistical features from both the time and frequency domains as statistical summaries of each signal. We assess the fault diagnosis capabilities of four open-sourced LLMs based on the FD-LLM framework, and evaluate the models’ adaptability and generalizability under various operational conditions and machine components, namely for traditional fault diagnosis, cross-operational conditions, and cross-machine component settings. Our results show that LLMs such as Llama3 and Llama3-instruct demonstrate strong fault detection capabilities and significant adaptability across different operational conditions, outperforming state-of-the-art deep learning (DL) approaches in many cases. © 2024, CC BY-NC-SA.

关键词： Shafts (machine components)

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Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization

arXiv

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arXiv 2024年

作者： Wang, Yangdi Zhang, Zhi-Hai Xu, Su Xiu Guo, Wenming School of Management Beijing Institute of Technology Beijing100081 China Department of Industrial Engineering Tsinghua University Beijing100084 China School of Management Beijing Institute of Technology Beijing100081 China School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China

Overfitting commonly occurs when applying deep neural networks (DNNs) on small-scale datasets, where DNNs do not generalize well from existing data to unseen data. The main reason resulting in overfitting is that small-scale datasets cannot reflect the situations of the real world. Label smoothing (LS) is an effective regularization method to prevent overfitting, avoiding it by mixing one-hot labels with uniform label vectors. However, LS only focuses on labels while ignoring the distribution of existing data. In this paper, we introduce the distributionally robust optimization (DRO) to LS, achieving shift the existing data distribution flexibly to unseen domains when training DNNs. Specifically, we prove that the regularization of LS can be extended to a regularization term for the DNNs parameters when integrating DRO. The regularization term can be utilized to shift existing data to unseen domains and generate new data. Furthermore, we propose an approximate gradient-iteration label smoothing algorithm (GI-LS) to achieve the findings and train DNNs. We prove that the shift for the existing data does not influence the convergence of GI-LS. Since GI-LS incorporates a series of hyperparameters, we further consider using Bayesian optimization (BO) to find the relatively optimal combinations of these hyperparameters. Taking small-scale anomaly classification tasks as a case, we evaluate GI-LS, and the results clearly demonstrate its superior performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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LGMRec: Local and Global Graph Learning for Multimodal Recommendation

arXiv

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arXiv 2023年

作者： Guo, Zhiqiang Li, Jianjun Li, Guohui Wang, Chaoyang Shi, Si Ruan, Bin School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China Wuhan Digital Engineering Institute Wuhan China Shenzhen China

The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals;(2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests. Copyright © 2023, The Authors. All rights reserved.

关键词： Behavioral research

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Towards automatic ethiopian endemic animals detection on android using deep learning 4

Towards automatic ethiopian endemic animals detection on and...

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4th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2021

作者： Endris, Ahmed Zakria Deng, Jianhua Ahmednasir, M. Mohammed, Juhar Kewyu, Nurit School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Wollo University Kombolcha Institute of Technology Kombolcha Ethiopia

ISBN: (纸本)9781665413220

Nowadays, deep learning became the dominant object recognition technique. Due to its outstanding feature extraction and adaptable capabilities, however, how to ensure speed and accuracy is still a significant task in the area of object detection. In this paper, we provide large-scale animals dataset for the object detection task. The animal's dataset consists six meaningful classes: chilada baboon, red fox, walia ibex, bale mountain vervet, black maned lion, and Somali wild ass. In addition, we utilize a You only look once (YOLO) v3 algorithm for animal detection. YOLOv3 is one of the most extensively used deep learning object detection algorithms;And we have developed user friendly graphical user interface (GUI) based mobile android application. Experiments are performed on our animal dataset that are collected from different positions. The results demonstrate that the proposed approach is highly efficient and effective which provides an accuracy of 97.24% on our animal dataset. © 2021 IEEE.

关键词： Object detection

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