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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Zhenguo Liu, Jianjian Li, Ying Yunnan Key Laboratory of Artificial Intelligence Kunming Kunming650500 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9789819783663

Pre-trained language representation-based dependency parsing models have achieved obvious improvements in rich-resource languages. However, these model performances depend on the quality and scale of training data significantly. Compared with Chinese and English, the scale of Vietnamese Dependency treebank is scarcity. Considering human annotation is labor-intensive and time-consuming, we propose a multi-features enhanced multi-task learning framework to convert all heterogeneous Vietnamese Treebanks to a unified one. On the one hand, we exploit Tree BiLSTM and pattern embedding to extract global and local dependency tree features from the source Treebank. On the other hand, we propose to integrate these features into a multi-task learning framework to use the source dependency parsing to assist the conversion processing. Experiments on the benchmark datasets show that our proposed model can effectively convert heterogeneous treebanks, thus further improving the Vietnamese dependency parsing accuracy by about 7.12 points in LAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi-task learning

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Development of an IoT-based Intelligent Energy Management System with Motion Notification on Telegram in a Smart Home 5

Development of an IoT-based Intelligent Energy Management Sy...

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5th International Conference on Information Management and Machine intelligence, ICIMMI 2023

作者： Kanwer, Budesh Mamodiya, Udit Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Rajasthan Jaipur India Department of Electrical Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India

ISBN: (纸本)9798400709418

This research paper focuses on the development of an intelligent energy management system based on the Internet of Things (IoT), emphasizing the imperative need to optimize energy consumption. Given the ubiquity of energy usage in various aspects of daily life and its essential role in our future sustainability, efficient energy management is of paramount importance. Our devised system utilizes IoT sensors to autonomously control the power supply to a room by detecting changes in user proximity and motion. Additionally, we have integrated a Telegram chatbot to provide real-time notifications about the status of lighting fixtures being turned on or off, ensuring homeowners are informed even when away from their residence. © 2023 ACM.

关键词： Internet of things

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Exploring Novel CNN Architectures for Deep Learning-Based Liver Tumor Occurrence Prediction

Exploring Novel CNN Architectures for Deep Learning-Based Li...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Kalaiselvi, P. Anusuya, S. Kumaravel, A. Esther, C. Gomathy, G. Aravind, Arun Sri Sairam Engineering College Department of Artificial Intelligence and Data Science India Simats Engineering Department of Computer Science and Engineering India Biher Department of Information Technology India

ISBN: (纸本)9798350394580

According to the authorized findings of,' Hepatocellular carcinoma (HCC)' a common in the human species, liver located, tumor,90 % of its occurrences demand any sort of support for predicting them in advance, if possible. For this purpose, Image Processing, Text Mining, and Deep learning techniques are handled with effective pre-processing methods in the recent research works to improve the results available so far. A majority of contribution by the researchers finds it challenging mostly in selecting suitable architectures for deep learning as most of the previous literature studies show the lacking of details on feasibility and justifiability in specifying the architectures. In this article, it is aimed at the primary goal of such studies as the first attempt on varying architectures in terms of profile and position of layers and observing the performance for recognizing the liver Tumor from the Image-data sets. The CNN architecture of neural network like AlexNet, VGGnet, ResNet, LeNet are standard architectures, based on this liver tumor dataset is implemented in customized architecture and its produced best performance with accuracy of 92.4 %. And It is compared with MeanderHandPD, Breast cancer and Liver Tumor datasets, the proposed architecture shows higher performance for Liver Tumor images. © 2023 IEEE.

关键词： AlexNet DeepLearning4j F-Measure Hepatocellular carcinoma Precision ROC SoftMax VGG

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PE-DeepNet: A deep neural network model for pulmonary embolism detection

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International Journal of Intelligent Networks 2022年 3卷 176-180页

作者： Lynch, Damian M, Suriya School of Engineering University of Technology Kingston Jamaica Department of Artificial Intelligence & Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Machine learning in medical image processing has shown to be a useful way for discovering patterns in both poorly labelled and unlabeled datasets. Venous thromboembolism, which includes deep vein thrombosis and pulmonary embolism, is a major cause of death in patients and requires quick detection by specialists. Using an artificial neural network, the suggested study was carried out to aid doctors in identifying and forecasting the risk level of pulmonary embolism in patients. This research presents a hybrid deep learning convolutional neural network model called PE-DeepNet (Pulmonary Embolism detection using Deep neural Network) to perform quick and accurate pulmonary embolism detection. The experiment uses a case study from the standard RSNA STR Pulmonary Embolism Chest CT scan image dataset. The proposed work results in an accuracy of 94.2%, an improvement over existing CNN models with minor trainable parameters. © 2022 The Authors

关键词： Convolutional neural networks

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Enhancing Accuracy: Iris Flower Classification with Ensemble Models 1

Enhancing Accuracy: Iris Flower Classification with Ensemble...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Mani, Nishanthini Murugiah, Premkumar Veeraraghavan, Vethavarna Razia, Nuzha Ramesh, Shashank Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science TamilNadu Coimbatore India NextLabs Pte. Ltd. Software Engineer 2 Singapore

ISBN: (纸本)9798350372748

This research study investigates the classification of Iris flowers based on their morphological structures, addressing the challenges posed by variations in attributes like size, shape, and color. This study explores various classification techniques and their practical implementations by conducting a comparative analysis using the IRIS dataset. With 21 attributes and three species (Setosa, Versicolor, and Virginica), each comprising 400 samples, this research aims to leverage machine learning algorithms to achieve precise classification. Key steps include data preprocessing, model selection, and hyperparameter tuning, with evaluation metrics to enhance the model performance. Furthermore, this study also explores ensemble-based learning to improve the prediction accuracy. Comparative analysis reveals the accuracy of each model, demonstrating effective predictions with accuracy rates of 95.5% and 97%. In summary, this research study offers a comprehensive overview of Iris flower species classification and prediction using machine learning techniques, contributing to advancements in this domain. © 2024 IEEE.

关键词： Classification (of information)

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Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

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SN Computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

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Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

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Computers, Materials & Continua 2024年第5期79卷 3355-3372页

作者： Jianfeng He Haowei Ye Jie Ning Hui Zhou Bo She Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan Key Laboratory of Artificial IntelligenceKunming650500China School of Physics and Electronic Engineering Yuxi Normal UniversityYuxi653100China PET/CT Center Affiliated Hospital of Kunming University of Science and TechnologyFirst People’s Hospital of Yunnan ProvinceKunming650031China

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion ***’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://***/yehaowei/temt.

关键词： PET lung scans respiratory motion correction triple equivariant motion transformer lie group motion decomposition

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Heart Disease Prediction using Machine Learning 3

Heart Disease Prediction using Machine Learning

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Telsang, Supriya Pokale, Tanvi Posugade, Anagha Patwal, Pradyot Shirode, Prachi Popade, Om Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vishwakarma Institute of Technology Department of Information Technology Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350364569

Heart attack and other heart diseases have acted as the major global cause of mortality. In accordance with WHO, about 32% death was due to cardiovascular disease, among which 85% was only due to heart disease There are various factors that lead to heart disease including high LDL, cholesterol, high blood pressure, Diabetes Smoking, Unhealthy diet, Obesity and Physical inactivity. If heart disease is not predicted earlier, it reduces patient's chances of survival. Therefore, it is essential to design a secure and precise system to predict heart disease early for proper treatment of patients. This research paper presents comprehensive analysis to identify heart disease using different predictive analytic methods. The research utilized a dataset of 1025 instances with 13 input parameters compromising age, sex, chest pain, blood pressure, blood sugar, electrocardiographic results, heart rate etc. The research tests and implements various predictive analytic methods such as Decision tree, Random Forest, Logistic regression and KN neighbor to predict the risk of heart disease on 60:40, 70:30 and 80:20 ratio data split. This research paper also highlights the superiority of Random Forest model for predicting heart disease with highest accuracy. © 2024 IEEE.

关键词： decision tree Heart disease KNN Logistic regression Randon Forest

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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science China(Information sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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EEG Information Transfer Changes in Different Daily Fatigue Levels during Drowsy Driving

IEEE Open Journal of Engineering in Medicine and Biology

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IEEE Open Journal of engineering in Medicine and Biology 2024年 5卷 180-190页

作者： Huang, Kuan-Chih Tseng, Chun-Ying Lin, Chin-Teng National Yang Ming Chiao Tung University Brain Science and Technology Center Hsinchu300 Taiwan University of Technology Sydney Australian Artificial Intelligence Institute Faculty of Engineering and IT SydneyNSW2007 Australia National Yang Ming Chiao Tung University Brain Science and Technology Center Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A significant issue for traffic safety has been drowsy driving for decades. A number of studies have investigated the effects of acute fatigue on spectral power;and recent research has revealed that drowsy driving is associated with a variety of brain connections in a specific cortico-cortical pathway. In spite of this, it is still unclear how different brain regions are connected in drowsy driving at different levels of daily fatigue. This study identified the brain connectivity-behavior relationship among three different daily fatigue levels (low-, median-and high-fatigue) with the EEG data transfer entropy. According to the results, only low-and medium-fatigue groups demonstrated an inverted U-shaped change in connectivity from high performance to poor behavioral performance. In addition, from low-to high-fatigue groups, connectivity magnitude decreased in the frontal region and increased in the occipital region. These study results suggest that brain connectivity and driving behavior would be affected by different levels of daily fatigue. © 2020 IEEE.

关键词： Electroencephalography

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