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A Study on Transcontinental Diabetes Datasets Using a Soft-Voting Ensemble Learning Approach 1

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Pradhan, Gaurav Thapa, Gopal Pradhan, Ratika Khandelwal, Bidita Visalakshi, S. Department of Computer Applications Sikkim Manipal Institute of Technology SMU Majitar Rangpo East Sikkim India Department of Computer Science and Engineering Sikkim Institute of Science and Technology Chisopani South Sikkim India Department of Medicine Sikkim Manipal Institute of Medical Sciences SMU Tadong East Sikkim Gangtok India

ISBN: (数字)9789819919833

ISBN: (纸本)9789819919826

Worldwide, diabetes mellitus (DM) is one of the primary causes of illness and death. Diabetes has a well-established hereditary link, which is widely acknowledged. Diabetes is a disease that occurs when the blood glucose level in the body is too high. Diabetes can result in serious cardiovascular problems. According to the WHO, there are 3.7 million fatalities by the age of 70, and cardiovascular disease is increasing day by day which leads to a high death rate (https://***/diabetes/basics/*** [16]). Therefore, the detection of diabetes at an early stage can help patients’ lives. Various research is going under the healthcare sector in the prediction of disease in the field of data mining. Data mining techniques are frequently utilized in early disease diagnosis. In this paper, five diabetes datasets have been used and the main objective is to compare the various machine learning algorithms for detecting and classifying diabetes. The algorithms used for classification are Random Forest, Logistic Regression, Naïve Bayes, XGBoost, and the proposed ensemble approach. The proposed ensemble approach provided better accuracy as compared with individual classification algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Fine-Grained Question-Answer Matching via Sentence-Aware Contrastive Self-supervised Transfer 12th

Fine-Grained Question-Answer Matching via Sentence-Aware Co...

引用

12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Wang, Jingjing Luo, Jiamin Zhou, Guodong School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9783031446924

Previous studies always consider the question-answer (QA) matching task as a one-to-one text matching problem. This study builds upon existing research by expanding the scope to a many-to-many mapping scenario and proposes a new fine-grained QA matching (FQAM) task, which aims to accurately predict the many-to-many matching relationship between all sub-questions and sub-answers within each QA pair. Particularly, a meticulously annotated corpus of high quality is constructed specifically for FQAM to facilitate this research. On this basis, owing to the challenge of expensive data annotation associated with FQAM, we propose a sentence-aware contrastive self-supervised transfer (SCST) approach to transfer the sentence alignment information pre-trained with massive unannotated QA pairs to assist in FQAM. Experimental evaluations conducted on our annotated corpus demonstrate the importance of utilizing sentence alignment information from unannotated QA pairs in FQAM and justify the effectiveness of our approach in capturing such information. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Alignment

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An Intelligent Self-Driving Car’s Design and Development, Including Lane Detection Using ROS and Machine Vision Algorithms 1

引用

3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Sujatha, E. Sundar, J. Sathiya Jeba Raju, D. Naveen Lakshminarayanan, S. Suganthi, N. Thandalam Chennai India Department of Computer Science and Engineering Velammal Institute of Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India Sri Sairam Engineering College Chennai India SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

It is challenging to find a solution for lane detection. It has aroused the curiosity of the computer vision field for many years. It has been found that computer vision and machine learning algorithms struggle to tackle the multi-feature identification problem known as lane detection. Even though there are a few different machine learning approaches that may be used for lane identification, these approaches are often employed for classification rather than feature development. On the other hand, contemporary techniques of machine learning may be used to discover features that have a high recognition value, and they have shown success in feature identification tests. These strategies haven’t been applied correctly, which compromises their efficiency and accuracy when it comes to lane recognition. In this study, we provide a fresh approach to solving the problem. A brand-new preprocessing and Region of Interest (ROI) selection method is presented in this article. The major objective is to extract white features by making use of the HSV color transformation, adding preliminary edge feature detection while doing preprocessing, and then selecting ROI based on the preprocessing that was proposed. With the help of this cutting-edge preprocessing strategy, the lane may be found. The integrated autonomous vehicle that we envision is one that is controlled by a Robotic Operating System and that is capable of making intelligent driving choices. The unique filtering and noise reduction techniques that were used on the visual feedback by means of the processing unit served as the basis for the digital image-processing algorithm that was responsible for the greatest performance achieved by the autonomous vehicle. Within the control system, we used two separate control units, one of which was a master and the other of which was a slave. The master control unit is in charge of the visual processing and filtering, while the slave control unit is in charge of the vehicle’s propulsio

关键词： Autonomous vehicles

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Lightweight Design and Property Analysis of Humanoid Robot Thigh Integrated Structure with Appearance 16th

Lightweight Design and Property Analysis of Humanoid Robot T...

引用

16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Nie, Daming Gu, Jason Zhang, Yu Jiang, Hongjian Research Center for Intelligent Robotics Zhejiang Lab Hangzhou310058 China Department of Electrical and Computer Engineering Dalhousie University HalifaxB3M 1A2 Canada

ISBN: (纸本)9789819964949

The lightweight and high stiffness leg structure of humanoid robots can effectively reduce rotational inertia and energy consumption, improving the robot's ability to quickly switch motion states. Based on the lightweight requirements of robots, this article proposes an innovative design method for humanoid robot legs based on the "structure-appearance" integration of light-alloy additive manufacturing technology. The load-bearing part of the leg structure is designed as a "shell + density-variable lattice" configuration, and the appearance part is set as a thin shell with equal thickness. The load-bearing part and the appearance one are combined together, resulting in a 20% weight reduction compared to traditional legs. The leg structure sample was processed using metal additive manufacturing method, and tested by a set of personalized fixture. When applying 200 Nm loading under pure torsion conditions, the local strain did not exceed 0.0175%, indicating that it meets the usage requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Anthropomorphic robots

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Segmentation of Brachial Plexus Ultrasound Images Based on Modified SegNet Model 24th

Segmentation of Brachial Plexus Ultrasound Images Based on...

引用

24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Yan, Songlin Chen, Xiujiao Tang, Xiaoying Chi, Xuebing Guilin Tourism University Guilin541006 China The Second Affiliated Hospital of Guilin Medical University Guilin541199 China Southern University of Science and Technology Shenzhen518055 China Computer Network Information Center Beijing100190 China

ISBN: (纸本)9783031482311

The images automatic segmentation is an important technique for medical treatment. It can help doctor to relieve from heavy works of reading ultrasound images, especially for brachial plexus images. Moreover, the deep learning technology assists doctors in locating the catheters and improves the efficiency and accuracy of injection. However, the research of brachial plexus ultrasonic image segmentation is too few to satisfy the needs of medical application. In this paper, we used a novel modified SegNet to accurately segment brachial plexus. In the training stage, the original training set was divided into two parts randomly (training set 90% and validation set 10%), and the parameters of models were determined and optimized by adopting cross-validation method and data augmentation which can avoid over-fitting effectively. Computational results show that, the model significantly increases nerve segmentation accuracy with 96%;meanwhile, the model is scored 0.644 by Kaggle competition (The Kaggle competition uses CSV files containing the final results for scoring. Besides, the Kaggle competition does not require participants to provide open source code, and all participants’ competition scores and rankings can be found on the website: https://***/c/ultrasound-nerve-segmentation/leaderboard ). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Medical applications

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Bark Texture Classification Using Deep Transfer Learning 16th

Bark Texture Classification Using Deep Transfer Learning

引用

16th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

作者： Bhusnurmath, Rohini A. Doddamani, Shaila Department of Computer Science Karnataka State Akkamahadevi Women’s University Karnataka Vijayapura586108 India

ISBN: (纸本)9783031364013

Tasks that are involved in forest are conservation, disease diagnostics, plant production, and tree species identification is crucial. There has been a disagreement over whether the leaves, fruits, flowers, or bark of the tree should be utilized to distinguish between species. Research has shown that bark is crucial because it persists during seasonal changes and gives trees their distinctive identities through structural variances. Using bark texture image to identify tree species is a difficult topic that could be helpful for many forestry-related jobs. Although recent developments of deep learning performed the outstanding outcome on common eyesight problems. This research presents a deep learning-based strategy for classifying 50 different categories of trees based upon the texture of their bark for that taken the dataset called BarkVN-50. This BarkVN-50 dataset is the largest values of trees bark that have been taken into account for bark classification thus far. Here in present experiment investigated the use feature extraction from the basic CNN model and pre-trained (Transfer learning techniques) models like VGG16, MobileNet and also compared the results of the all three models with their computation time and accuracies of each models. It is observed from results that pre-trained model work more efficient with high accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forestry

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Towards Improving the Packet Delivery Ratio in Mobile Ad Hoc Networks Using DSDV Protocol and Cuckoo Search Algorithm

引用

International Virtual Conference on Industry 4.0, IVCI4.0 2021

作者： Devi, T. Ganesan, R. Deepa, N. Jaisharma, K. Department of Computer Science and Engineering Saveetha School of Engineering SaveethaInstitute of Medical and Technical Sciences Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811999888

A wireless ad hoc network which is infrastructure less where the communication of the mobile nodes is done through wireless mode without any base station is termed MANET (Mobile Ad hoc Network). One of the main issues of MANETs is routing due to the free movement of nodes and changing topology. As a result of this, there is a change in topology due to failure in the routes thereby decreasing the network performance. Forwarding of data between the transmitter and the receiver is the main aim of routing. Node connection can be made at any time and data can be forwarded to the specifically selected nodes. There is a need for developing an effective routing protocol to find the shortest path for packet forwarding. The proposed work employs cuckoo search algorithm along with destination sequenced distance vector to identify routing paths that are shorter to forward the data packets. In order to perform validation, the proposed work is compared with ad hoc on-demand distance vector routing protocol to show the efficacy of the system. The packet delivery ratio of the proposed system is high which makes the DSDV protocol to be suitable for various communication. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

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Secrecy Rate Optimization for Energy Efficient Cognitive Relay Networks 1

引用

10th International Conference on Innovations in computer science and Engineering, ICICSE 2022

作者： Alam, Md. Khorshed Saha, Trina Department of Computer Science & Engineering State University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

We are concerned about the secrecy of power efficient transmission in an underlay cognitive relay network. We recommend a cooperative cognitive relay model in presence of a primary consumer. By using the use of the multi-relays equipped with single antenna, a secondary source and a secondary destination try to communicate with each other in presence of an eavesdropper. We formulated an optimization problem to maximize the secrecy efficiency (SE) and energy efficiency (EE) of the proposed version retaining in consideration of data rate and transmit power of the cognitive communication as well as interference problem with the primary user. Our formulated problem is a non-convex problem. We recognize that there are techniques to resolve a non-convex problem, such as one is relaxation technique and another one is randomization technique. Both the approach can give us invalid answer, so we brought a rank one constraint with main problem that is considered as a penalty approach and solved the problem by way of the use of an iterative set of rules which is called active-set algorithm. We furnished the numerical and graphical representation of proposed scheme result and tried to show a contrast of effectiveness between proposed cooperative version and present MIMO cognitive radio network model. The result shows individual secrecy rate at destination in proposed model which is more than the present MIMO model. However, overall secrecy rate is higher in the MIMO model than the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Automatic Model for Relation Extraction from Text Documents Using Deep Learning Neural Network 1

引用

9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2021

作者： Lavanya, B. Sasipriya, G. Department of Computer Science University of Madras Chennai India

ISBN: (数字)9783031215179

ISBN: (纸本)9783031215162

Relation extraction is a significant stage in the information extraction process as it establishes the semantic relation between entities. In nanoparticles, relation extraction is vital, especially in nanomedicine, where the benefits are substantial. The necessity for automatic relation extraction in the field of nanoparticles is an imminent need, as human data extraction is nearly impossible due to the ever-growing number of scientific papers on this subject. Advanced technologies and new research approaches to improve and refine the properties of nanoparticles have emerged in recent years. Our aim is to train and build a model for the automatic extraction of relations from research articles. This paper summarizes how a framework was developed for a relation extraction system for nanoparticles by collecting information from existing research articles, training the model, and using the Natural Language Processing model on the relations of extracted text. An open-source Natural Language Processing model framework formed the basis for our implementation, and it received a significant F1 score for each threshold. © 2022, Springer Nature Switzerland AG.

关键词： Nanoparticles

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Nuclei Segmentation Approach for computer Aided Diagnosis 16th

Nuclei Segmentation Approach for Computer Aided Diagnosis

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16th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

作者： Darapaneni, Narayana Paduri, Anwesh Reddy Gulani, Jayesh Aithu, Sanath Santhosh, M.M. Varghese, Shaji Northwestern University/Great Learning Chicago United States Great Learning Bangalore India PES University Bangalore India

ISBN: (纸本)9783031364013

computer aided diagnosis based on computational pathology combines the concepts of pathology with computer science to develop automated mechanisms for interpretation of histological images. Nuclei segmentation is a type of computation pathology that enables the identification and separation of Nuclei cells in a histopathology image. These segmentation routines play a critical role in quantification of cellular structures and provide insights to identify abnormalities related to pathological conditions such as cancer. In order to determine the progress and severity of a pathological condition it is necessary to use tools that measure the properties of tissue structure in different stages of a disease. Biomedical image analysis for Nuclei segmentation using deep learning methodologies is an ongoing area of research wherein to improve histopathology examination it is desired to develop a model that can generalize and accurately segment different types of nuclei. However, creating a suitable model for this process is difficult due to variability in patient data, overlapping nuclei cell boundaries, image quality, background noise, and provision of well-annotated data. In this study, we describe our findings on the performance of a model developed using the standard U-Net architecture and compare it against a model that is developed based on pre-trained weights and models using ImageNet as the backbone. The aim is to develop a semantic segmentation model that can capture high-level information in terms of features from a data set that consists of Whole-slide images (WSI). The outcome of the research is to be able to select a semantic segmentation model that is efficient and fast to classify Nuclei cells for the given biomedical tissue images. The model uses a stochastic gradient descent optimizer and the evaluation criteria for this network i.e., the ground truth against prediction is based on Mean Intersection over Union. © 2023, The Author(s), under exclusive license to

关键词： Pathology

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